No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Inspyred: Bio-inspired algorithms in Python
... The goal is to search for the optimal resources to run the applications on the optimal resources to minimize the end-to-end latency of the applications. The study [3,4,5] suggested a bio-inspired robotics agent who takes actions 65 on available resources in the system [6,7]. The goal was to minimize the resource cost of the applications during the performance. ...
... • Baseline3: The local search enabled bio-inspired scheme which is widely exploited in [5,6,7] studies 220 deployed in the simulation environment. The goal is to minimize the energy and lateness of the services in the system. ...
... This part discusses the obtained results of the proposed system and its approaches compared to baseline approaches to solve the problem. To evaluate the RPD% of all existing heuristics, the study evaluated the [7,9] This Markov Decision enabled MMCT approach widely implemented by these studies to solve the scheduling problem in fog-cloud environment. The considered workload was considered as the workflow healthcare application. ...
Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems
... In this section, we describe how we implemented and optimized the models of the six neurons. The models of the ionic currents and whole neurons are implemented in NEURON [72,73] and solved in Python. For each neuron, the parameters describing the activation and inactivation (and the corresponding time constants) of the ionic currents were used as fixed parameters, while the conductance values, representing the relative weights of the currents, were used as free parameters in the optimization procedure. ...
... Moreover, in the optimization procedure, we adjusted the reversal potential of the leakage current and the membrane capacitance. To obtain the optimal set of parameters, we used a hybrid optimization strategy that combines evolutionary computation [73], using the Python library Inspyred (https://pypi.org/project/inspyred/), and least square minimization of SciPy [74]. During the optimization procedure the HH equations are solved with NEURON. ...
The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C . elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes of C . elegans neurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling of C . elegans neuronal networks and virtual experiments on the nematode nervous system.
... The RSMs hyper-parameters of nugget size, correlation function and regression model were optimised for the best response of the model. The optimisation is based on a global single-objective genetic algorithm [41] and is configured with 100 generations, an initial population of 50 times the number of design parameters and a population size per generation of 25 times the number of DOF. These settings were tuned to ensure the convergence of the optimisation. ...
... Two methods of multiobjective evolutionary optimization (EMO) were used to reproduce the dynamics of TC neurons in the biophysical model. Namely: genetic algorithms with nondominated sorting (NSGA2) (Deb et al., 2002;Deb, 2001), implemented in the Python library inspyred by Dr. Aaron Garrett (Garrett, 2012;Tonda, 2020) and developed in-house genetic algorithm with Krayzman's adaptive multiobjective optimization (see Apendix -Genetic algorithm with Krayzman's adaptive multiobjective optimization (KAMOGA)). NSGA2 uses the Pareto archival strategy, selecting one model over another if it is better than or equal to the other model in all fitness functions and strictly better in at least one fitness function. ...
The developing visual thalamus and cortex extract positional information encoded in the correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of connectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit refinement period to explore the role of synaptic and circuit properties in the regulation of such neural correlations. We find that the NMDA receptor dominance, combined with weak recurrent excitation and inhibition characteristic of this age, prevents the emergence of spike-correlations between thalamocortical neurons on the millisecond timescale. Such precise correlations, which would emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the spatial information contained by thalamic spikes, and therefore we term them 'parasitic' correlations. Our results suggest that developing synapses and circuits evolved mechanisms to compensate for such detrimental parasitic correlations arising from the unrefined and immature circuit.
... 12). The maximisation of this function is conducted with a Genetic Algorithm (GA) optimisation method (Tonda 2019). The implementation of the co-Kriging methodology used in this investigation has been validated with benchmark functions . ...
Purpose - Aerodynamic shape optimisation is complex due to the high dimensionality of the problem, the associated non-linearity and its large computational cost. These three aspects have an impact on the overall time of the design process. To overcome these challenges, this paper develops a method for transonic aerodynamic design with dimensionality reduction and multi-fidelity techniques.
Design/methodology/approach - The developed methodology is used for the optimisation
of an installed civil ultra-high bypass ratio aero-engine nacelle. As such, the effects of airframe-engine integration are considered during the optimisation routine. The active subspace method is applied to reduce the dimensionality of the problem from 32 to 2 design variables with a database compiled with Euler CFD calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and RANS higher-fidelity CFD evaluations.
Findings - Relative to a baseline aero-engine nacelle derived from an isolated optimisation
process, the proposed method yielded a non-axisymmetric nacelle configuration with an increment in net vehicle force of 0.65% of the nominal standard net thrust.
Originality - This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multi-fidelity method, and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.
... Indeed, several researchers believe that Artificial General Intelligence (AGI) can only be achieved through the emergence of high-level reorganizations that result from the bottom-level interactions of a multiagent complex system. This concept of reorganization in algorithmic architectures has been led mainly by the field of bio-inspired evolutionary optimization systems [10][11][12][13]. In fact, the terrestrial natural mechanisms, that have been perfected over millions of years, are the best example of intelligent behaviors (and the only) that we know of. ...
The CEFYDRA is a network of units whose outputs are obtained using a fuzzy Takagi-Sugeno-Kang approach. At each unit, the information is clustered in fuzzy sets and then mapped using logistic functions and Cauchy membership functions. There are two primary contributions in this paper. The first is a set of suggestions for the initialization criteria of the parameters of a CEFYDRA. The second is a proposal for the self-reorganizing algorithm that modifies the location of the clusters of each unit as the algorithm is trained with gradient descent.
... The previously introduced core-shell force field [9], specifically parameterized for LATP, is extended by Buckingham parameters for the Mg 2+ -ion interactions. A Particle Swarm Optimizer [35] is employed for global optimization via energy and force matching against first principles doped reference data. The DFT reference calculations are obtained using the CASTEP [36] plane wave code along with the PBE exchange-correlation functional [37] and ultrasoft pseudopotentials as provided by the GBRV library [38]. ...
While great effort has been focused on bulk material design for high-performance All Solid-State Batteries (ASSBs), solid-solid interfaces, which typically extend over a nanometer regime, have been identified to severely impact cell performance. Major challenges are Li dendrite penetration along the grain boundary network of the Solid-State Electrolyte (SSE) and reductive decomposition at the electrolyte/electrode interface. A naturally forming nanoscale complexion encapsulating ceramic Li1+xAlxTi2−x(PO4)3 (LATP) SSE grains has been shown to serve as a thin protective layer against such degradation mechanisms. To further exploit this feature, we study the interfacial doping of divalent Mg2+ into LATP grain boundaries. Molecular Dynamics simulations for a realistic atomistic model of the grain boundary reveal Mg2+ to be an eligible dopant candidate as it rarely passes through the complexion and thus does not degrade the bulk electrolyte performance. Tuning the interphase stoichiometry promotes the suppression of reductive degradation mechanisms by lowering the Ti4+ content while simultaneously increasing the local Li+ conductivity. The Mg2+ doping investigated in this work identifies a promising route towards active interfacial engineering at the nanoscale from a computational perspective.
... Indeed, several researchers believe that Artificial General Intelligence (AGI) can only be achieved through the emergence of high-level reorganizations that result from the bottom-level interactions of a multiagent complex system. This concept of reorganization in algorithmic architectures has been led mainly by the field of bio-inspired evolutionary optimization systems [10][11][12][13]. In fact, the terrestrial natural mechanisms, that have been perfected over millions of years, are the best example of intelligent behaviors (and the only) that we know of. ...
The CEFYDRA is a network of units whose outputs are obtained using a fuzzy Takagi-Sugeno-Kang approach. At each unit, the information is clustered in fuzzy sets and then mapped using logistic functions and Cauchy membership functions. There are two primary contributions in this paper. The first is a set of suggestions for the initialization criteria of the parameters of a CEFYDRA. The second is a proposal for the self-reorganizing algorithm that modifies the location of the clusters of each unit as the algorithm is trained with gradient descent.
... These additional constraints prevent the use of gradient-based techniques and, therefore, we solved MOP (12) using the evolutionary algorithm NSGA-II (Deb et al., 2002), widely used in multi-objective optimization, which gradually approaches a well-distributed set of Pareto optimal solutions across the Pareto frontier and can be applied to continuous as well as to combinatorial search spaces (Emmerich and Deutz, 2018). Specifically, we used the implementation of this algorithm in the Python library Inspyred 1.0 (Tonda, 2020), with appropriate mutation and recombination operators for integer representation (Eiben and Smith, 2015, pp. 52-56). ...
In this study we present several multi-objective models for forest harvest scheduling in forest with single-species, even-aged stands using a continuous formulation. We seek to maximize economic profitability and even-flow of timber harvest volume, both for the first rotation and for the regulated forest. For that, we design new metrics that allow working with continuous decision variables, namely, the harvest time of each stand. Unlike traditional combinatorial formulations, this avoids dividing the planning horizon into periods and simulating alternative management prescriptions before the optimization process. We propose to combine a scalarization technique (weighting method) with a gradient-type algorithm (L-BFGS-B) to obtain the Pareto frontier of the problem, which graphically shows the relationships (trade-offs) between objectives, and helps the decision makers to choose a suitable weighting for each objective. We compare this approach with the widely used in forestry multi-objective evolutionary algorithm NSGA-II. We analyze the model in a Eucalyptus globulus Labill. forest of Galicia (NW Spain). The continuous formulation proves robust in forests with different structures and provides better results than the traditional combinatorial approach. For problem solving, our proposal shows a clear advantage over the evolutionary algorithm in terms of computational time (efficiency), being of the order of 65 times faster for both continuous and discrete formulations.
... PySwarms [11] is a research toolkit for implementing the PSO algorithm. In [21], the author developed a new Python module called Inspyred 1.0 for implementing several evolutionary algorithms like GA. The authors of EvoloPy recently extended their Python framework to develop EvoloPy-FS [10] which mainly focused on FS problems and included several well-known Swarm Intelligence (SI) algorithms like PSO, WOA and CS. ...
In today’s data-driven world, every workforce is relentlessly exploiting the power of data to get that extra edge in order to triumph over the others. However, there is a saying that goes like, “Work smarter, not harder.” Studies have shown that the amount of data people actually use is way smaller than the data being generated. This very fact gives rise to an important research topic, called dimension reduction, which is one of the smartest (not hardest) strategies for retrieving useful information (here, features) from a given high-dimensional dataset. Feature selection (FS) is among the best dimension reduction tactics available in the literature. To this end, in this paper, we have made an effort to introduce an FS framework called Py_FS that we have developed to simplify the task for the researchers. Py_FS currently provides an interesting combination of 12 wrapper- and 4 filter-based FS techniques along with various evaluation metrics. For converting the continuous search space to a binary search space, three transfer functions have been used. The algorithms have been experimented on two Microarray and four UCI datasets. To the best of our knowledge, it is the first ever framework to provide wrappers, filters, and evaluation metrics under one structure. The framework is highly flexible and can easily cater to the needs of the FS researchers. It is publicly hosted at the following link: Py_FS: A Python Framework for FS.
... For this reason, we exploited a genetic algorithm (GA), which is an evolutionary algorithm that mimics the process of natural selection. 54,55 In particular, we used the Python module inspyred, 56,57 where the parameter space ({χ, η}) was restrained to the [0,1] interval. ...
Despite the potentialities of the quantum mechanics (QM)/fluctuating charge (FQ) approach to model the spectral properties of solvated systems, its extensive use has been hampered by the lack of reliable parametrizations of solvents other than water. In this paper, we substantially extend the applicability of QM/FQ to solvating environments of different polarities and hydrogen-bonding capabilities. The reliability and robustness of the approach are demonstrated by challenging the model to simulate solvatochromic shifts of four organic chromophores, which display large shifts when dissolved in apolar, aprotic or polar, protic solvents.
... These additional constraints prevent the use of gradient-based techniques and, therefore, we solved MOP (12) using the evolutionary algorithm NSGA-II (Deb et al., 2002), widely used in multi-objective optimization, which gradually approaches a well-distributed set of Pareto optimal solutions across the Pareto frontier and can be applied to continuous as well as to combinatorial search spaces (Emmerich and Deutz, 2018). Specifically, we used the implementation of this algorithm in the Python library Inspyred 1.0 (Tonda, 2020), with appropriate mutation and recombination operators for integer representation (Eiben and Smith, 2015, pp. 52-56). ...
In this study we present several multi-objective models for forest harvest scheduling in forest with single-species, even-aged stands using a continuous formulation. We seek to maximize economic profitability and even-flow of products, both for the first rotation and for the regulated forest. For that, we have designed new metrics that allow working with continuous decision variables. We propose to combine an scalarization technique (weighting method) with a gradient-type algorithm (L-BFGS-B) to obtain the Pareto frontier of the problem. This a posteriori articulation of preferences allows a graphical display of the relationships (trade-offs and synergies) between objectives, helping the decision makers to choose a suitable weighting for each objective. We compare this approach with the widely used in forestry multi-objective genetic algorithm NSGA-II. We analyze the model in a Eucalyptus globulus Labill. forest of Galicia (NW Spain). The continuous formulation has proved robust in forests with different structures and provided better results than the traditional combinatorial approach. For problem solving, our proposal has shown a clear advantage over the evolutionary algorithm in terms of computational time (efficiency).
... 15-19], weights were taking equally spaced, and the L-BFGS-B algorithm was used again without any multi-start procedure. To check this approach, BOP (13) was also solved by the evolutionary algorithm NSGA-II [15], widely used in multiobjective optimization, and already implemented in the Python library Inspyred 1.0 [38]. ...
Background: Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest. For this, many mathematical formulations have been proposed, both within the linear and non-linear programming frameworks, in the latter case generally considering integer variables in a combinatorial manner. We present a novel approach for planning the management of forests comprising single-species, even-aged stands, using a continuous, multi-objective formulation (considering economic and even flow) which can be solved with gradient-type methods.
Results: The continuous formulation has proved robust in forest with different structures and different number of stands. The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems, both in terms of computational time (efficiency) and in the solution obtained (effectiveness): their improvement increases drastically with the dimension of the problem (number of stands).
Conclusions: It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models. The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques, does not require to set management prescriptions in advance, and avoids the division of the planning horizon into periods, providing better solutions than the traditional combinatorial formulations. The graphical display of trade-off information allows a posteriori articulation of preferences in an intuitive way, therefore being a very interesting tool for the decision-making process in forest planning.
... Currently, the EAs are implemented by the Inspyred (Tonda, 2020) and JMetalPy (Benítez-Hidalgo et al., 2019) Python libraries. ...
Metabolic Engineering aims to favor the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, that covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms.
Availability and Implementation
MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.
Supplementary information
Supplementary data are available at Bioinformatics online.
... The implementation of the EA was conducted through the use of the inspyred library, one of the most adopted frameworks for bio-inspired computation [25]. The library consists of a series of modules that implement all the main features of Evolutionary Algorithms as well as several other bio-inspired algorithms. ...
In recent years, the advent of new hardware and software technologies for navigation and control has made Unmanned Aerial Vehicles (UAVs) ever more autonomous and efficient. As a consequence, it is now possible to have drones moving within complex environments, such as cities or indoor areas. One of the main requirements for intelligent mission planning in such environments is the ability to correctly and efficiently detect and avoid obstacles. For this reason, various libraries have been created for the simulation of UAV navigation in virtual environments, in order to test algorithms for automatic obstacle detection and collision avoidance before deploying the drones in the real world. Usually, the performance of these algorithms depends on various parameters as well as specific application settings. However, while different parameter configurations can be easily tested in simulation, their number can be too large to allow a complete exploration of the parameter space or a manual tuning. Furthermore, a full analytical model of the parameters' influence on the algorithmic performance can be hard to obtain. Yet, it is extremely important to find their optimal values to allow collision-free navigation. In this direction, we propose here a thorough exploration, based on an Evolutionary Algorithm (EA), of the parameter space of the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. Our results show that the proposed EA is a viable solution for finding optimal parameter settings that can be generalizable to different scenarios characterized by different complexity levels.
... The construction of a related database containing known benchmarks is highly recommended for this purpose. At the time, repositories exist where nature-inspired or evolutionary algorithms have been implemented, such as DEAP (Fortin et al. 2012), EvoloPy (Faris et al. 2016), NiaPy (Vrbančič et al. 2018), jMetalPy (Benítez-Hidalgo et al. 2019), PySwarms (Miranda 2018) and Inspyred (Tonda 2020). However, most of them do not include benchmark optimization functions to test these approaches. ...
In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing nature-inspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new nature-inspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms.
... The NetPyNE tool also allows users without programming expertise to build sophisticated brain models using either the declarative format or the graphical user interface. NetPyNE supports two parameter optimization methods: grid search parameter sweep and evolutionary algorithms (using the Inspyred package [22]). In both cases, NetPyNE generates the job scheduler (Slurm or PBS Torque) scripts corresponding to a simulation with a specific set of parameters and and submits the required jobs. ...
Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resulting brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations, with each simulated second of the full circuit model requiring approximately 50 cores hours. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.
During development, retinal axons create broad and imprecise connections in the thalamus. This topology, very different from adults, supplies developing thalamic neurons with locally homogeneous synaptic currents and should cause spike correlation between thalamocortical neurons on a millisecond timescale. Such correlations have not been observed in vivo , at these ages, and would likely be maladaptive. Here, we use a biophysical model of the visual thalamus with the membrane and synaptic properties of 7-10 day-old mice to show that the developmentally appropriate dominance of NMDA-receptor currents and absence of strong recurrent inhibitory and excitatory connections prevents precise correlation and preserves topographic information in thalamic spikes. We illustrate possible reasons for this desynchronization using a phenomenological cortical model, which shows impaired network diversity when driven with precisely correlated inputs. Our results suggest that developing synapses and circuits evolved mechanisms to compensate for detrimental, “parasitic” correlation arising from the unrefined and immature circuit.
Computational screening methods have changed the way new materials and
processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and
parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.
Background: Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest. For this, many mathematical formulations have been proposed, both within the linear and non-linear programming frameworks, in the latter case generally considering integer variables in a combinatorial manner. We present a novel approach for planning the management of forests comprising single-species, even-aged stands, using a continuous, multi-objective formulation (considering economic and even flow) which can be solved with gradient-type methods.
Results: The continuous formulation has proved robust in forest with different structures and different number of stands. The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems, both in terms of computational time (efficiency) and in the solution obtained (effectiveness). Their improvement increases drastically with the dimension of the problem (number of stands).
Conclusions: It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models. The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques. The advantages of the new methodology are summarized as: it does not require to setmanagement prescriptions in advance, it avoids the division of the planning horizon into periods, and it provides better solutions than the traditional combinatorial formulations. Additionally, the graphical display of trade-off information allows an a posteriori articulation of preferences in an intuitive way, therefore being a very interesting tool for the decision-making process in forest planning.
In recent years, the evolutionary algorithms used in the solution of NP-Hard problems have become increasingly important. In addition, platforms and application development languages have diversified and started to be differentiated according to their intended use. However, the selection of an appropriate model development environment has become an important decision problem. This study guides the selection of suitable tools for optimization problems, especially in management science. The main objective is to identify the key attributes of the frameworks from the researcher’s point of view in management science and assign a total utility score to measure the relative importance of frameworks for evolutionary algorithms. For that reason, we propose a conjoint analysis model upon the preferences of management scientist for the appropriate framework that meets the needs in optimization problems.
We also aim at providing effective usage of relevant frameworks for appropriate types of problems, facilitating the work of researchers and therefore increasing the quality of the optimization procedure. By doing so, losing time and effort resulting from the wrong platform and framework selection, as well as ineffective model results, will be avoided. Moreover, the frameworks are also evaluated by calculating the weights of criteria with one of the recent multi-criteria decision-making method called Euclidean best–worst method and compared with the findings obtained from conjoint analysis. This study not only provides review of existing software tools developed for optimization problems but also contributes to research and practice in the field of optimization algorithms in general and helps the researchers in management science for meeting their needs while searching for the appropriate framework.
https://www.mdpi.com/1996-1073/13/12/3236 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Power systems are susceptible to disturbances due to their nature. These disturbances can cause overloads or even contingencies of greater impact. In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system. However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load. To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO). This optimization seeks to minimize the amount of load to be cut off. Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements. Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem. Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study. To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week. Results show that it is possible to determine the optimal unattended power of the system. This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made.
We give a critical assessment of the DEAP (Distributed Evolutionary Algorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionary algorithms including both strongly and loosely typed Genetic Programming, Genetic Algorithm, and Multi-Objective Evolutionary Algorithms such as NSGA-II and SPEA2. It contains most of the basic functions required by evolutionary computation, so that its users can easily construct various flavours of both single and multi-objective evolutionary algorithms and execute them using multiple processors. It is ideal for fast prototyping and can be used with an abundance of other Python libraries for data processing as well as other machine learning techniques.