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The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units h...
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The firefly algorithm (FA) is a swarm intelligence algorithm capable of solving global optimization problems exactly; it has been used to solve many practical problems. However, traditional firefly algorithms solve complex optimization problems with a simple update method, which leads to premature stagnation due to the limitation of firefly diversi...
Liquid loading is an undesired phenomenon in gas wells that occurs when producing wells attain a flow rate below which liquid will not be able to flow to the surface. The inability of the energy from the gas to transport the liquid to the surface causes back flow and eventual accumulation of liquid at the wellbore. This is characterised by intermit...
The standard firefly algorithm is suffered from three major drawbacks. Firstly, imbalanced exploration and exploitation due to random initial solution generation. Secondly, the local convergence rate is low when the randomization factor is large. Thirdly, low quality local and global search capability at termination stage that result in failing to...
This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the ot...
Firefly Algorithm (FA) is one of the new natural inspired optimization algorithms. It is inspired by the flashing behavior of the fireflies. Firefly algorithm, has some drawbacks such as getting trapped into several local optima, FA parameters are set fixed without change during iterations time. Besides that, it does not memorize or remember the hi...
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... It takes the feasible solution to the problem by checking its neighbours in finding an improved solution A memory structure is used to hance the performance of TS, where it does not consider visiting the previously visited solution. It is marked as "Taboo" if violated a rule [39]. The pseudocode of TS is shown in Fig.2. ...
The purpose of this paper is to test the fairness issue in the nurse scheduling problem. The process must adhere to the nurse scheduling policies. With the use of metaheuristic algorithms that offers a high-quality solution in a reasonable amount of time for real-world situations; they are a fascinating field of research that has made significant advances in solving optimization problem. One of the newly methods in optimization are Swarm Intelligence algorithms, they were abstracted from the law of population cooperation providing a new way to solve problems. This paper explored the combination of Firefly Algorithm, a population-based algorithm, and Tabu Search, a local search-based algorithm. The FA and FA-TS experiment was tested and evaluated using two iteration stages to determine the best solution, initial and best fitness values, the improvements of fitness values, the number of movements, and execution time. The computational results obtained are compared from the standard FA and proposed FA-TS method. The comparison showed improved results that FA-TS evaluated the efficiency of the proposed technique compared to the standard FA in most criteria comparisons.
... Duman et al. (2010) have try to resolve the fuel cost problem with the Gravitational Search method considering the valve-point effects. Subramanian et al. (2013), using Modified Firefly Algorithm, sought a solution to the optimization of load dispatch for 3 and 13 unit systems with valve point loading effects. Basetti et al. (2021) applied the Quasi-Oppositional-Based Political Optimization method to the fuel cost optimization problem considering the valve-point loading effect and presented the results comparatively. ...
... GSA (Duman et al., 2010) 300.2102 149.7953 399.9958 8234.10 MFA (Subramanian et al., 2013) 300.000 151.450 399.550 8231.13 TLBO (Tefek et al., 2018) 300.2651 149.7356 400 8234.08 ...
Abstract:In medical laboratories the product of the work process is the result issued to the patient. The laboratory result is an integral part of the overall diagnostic procedures for almost all pathological conditions and diseases. Laboratory medicine is the mainstay of medical treatment, diagnosis and prevention. Laboratory diagnostics influences 70-80% of the decision for hospital care and in terms of costs it covers only 3-5% of the total costs for health care. The laboratory result is used in monitoring the development of many diseases, monitoring the success of the therapy and as a confirmation of the cure. It is also used in the prevention of certain pathological conditions and diseases. Doctors of medicine from all specialties refer patients to medical laboratories on a daily basis for individual laboratory tests. Based on the results obtained, they complete the overall diagnostic picture for their patients. Together with the results of other clinical trials they diagnose the disease for their patients. Also, based on the obtained results, appropriate therapy is determined and the success of its application is monitored, primarily the drug therapy is sent together with the possible side effects from its application. Certain laboratory results are a mandatory indicator of the state of health in the employment of certain jobs, ie, to perform certain functions (police, army, fire service, sports workers, drivers of all vehicles, etc.). They can also represent court results and evidence.
At the beginning of the 21st century, in order to improve the quality of health and social care, priorities in laboratory medicine were defined, including laboratory automation, laboratory consolidation, molecular diagnostics, scanning, POCT (on-site examination), planning of costs and accreditation. Accreditation is one of the ways to regulate the quality of health care. Because accreditation is a tool that demonstrates the competence of medical laboratories and confirms the quality of the services they provide, medical laboratories can formally validate their competence through accreditation of testing methods. At present in Europe, the concept of accreditation of medical laboratories in accordance with ISO 15189 is widely accepted, and regulatory policies are focused on how accreditation can help ensure health and social care.
Keywords: accreditation, laboratory diagnostics, health care, ISO 15189
... With a power demand of 1263 MW, a six-unit test system without valve-point load effect was taken from [92], which is given in Table A5. The loss coefficients matrix was zero. ...
Slime Mould Algorithm (SMA) is a newly designed meat-heuristic search that mimics the nature of slime mould during the oscillation phase. This is demonstrated in a unique mathematical formulation that utilizes adjustable weights to influence the sequence of both negative and positive propagation waves to develop a method to link food supply with intensive exploration capacity and exploitation affinity. The study shows the usage of the SM algorithm to solve a non-convex and cost-effective Load Dispatch Problem (ELD) in an electric power system. The effectiveness of SMA is investigated for single area economic load dispatch on large-, medium-, and small-scale power systems, with 3-, 5-, 6-, 10-, 13-, 15-, 20-, 38-, and 40-unit test systems, and the results are substantiated by finding the difference between other well-known meta-heuristic algorithms. The SMA is more efficient than other standard, heuristic, and meta-heuristic search strategies in granting extremely ambitious outputs according to the comparison records.
... This method is easy to implement due to its fewer parameters, excellent ability to search, and effective convergence rate. The performance of the proposed algorithm in terms of the optimal solution has been compared with artificial bee colony (ABC) [14], standard flower pollination algorithm (FPA), and other discussed algorithms. This paper has divided into main five parts. ...
span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions. However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems. The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span
... Other authors have made comparisons and analysis between this approach and conventional methods without these variables [35]. On the other hand, some works employs heuristic approaches including deterministic and stochastic methods (e.g Montecarlo simulation) to solve the optimal dispatching [36][37][38][39][40][41]. ...
The power system operation considering energy storage systems (ESS) and renewable power represents a challenge. In a 24-hour economic dispatch, the generation resources are dispatched to meet demand requirements considering network restrictions. The uncertainty and unpredictability associated with renewable resources and storage systems represents challenges for power system operation due to operational and economical restrictions. This paper develops a detailed formulation to model energy storage systems (ESS) and renewable sources for power system operation considering 24-hour period. The model is formulated and evaluated with two different power systems (i.e. 5-bus and IEEE modified 24-bus systems). Wind availability patterns and scenarios are used to assess the ESS performance under different operational circumstances. With regard to the systems proposed, there are scenarios in order to evaluate ESS performance. In one of them, the increase in capacity did not represent significant savings or performance for the system, while in the other it was quite the opposite especially during peak load periods.
... Othman et al. [42] has modified FA by changing and in each iteration and by setting = 1. Subramanian et al. [43] has reported a modification on FA by adjusting by using min , min , iteration, and maximum generation. Liu et al. [44] have modified the parameters and of FA by using { a , e , iteration, and maximum genera-tion} and { a , e , iteration, and maximum generation} respectively. ...
Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.
... Both attraction and random movement = 0 e − r by changing r to r K √ n(Range) , and Winking probability p w = 0.5 + 0.1count i [54] Both attraction and random movement Minimum variation distance in-place of Cartesian distance [55] Both attraction and random movement = where r max = max d x i , x j |∀i, j [56] Both attraction and random movement , in every iteration according to location and maximum power per location, and = 1 [57] Both attraction and random movement ...
Firefly Algorithm (FA) is one of the popular algorithm of Swarm Intelligence domain that can be used in most of the areas of optimization. FA and its variants are simple to implement and easily understood. These can be used to successfully solve the problems of different areas. Modification in original FA or hybrid FA algorithms are required to solve diverse range of engineering problems. In this paper, a comprehensive review of firefly algorithm is presented and various characteristics are discussed. The various variant of FA such as binary, multi-objective and hybrid with other meta-heuristics are discussed. The applications and performance evolution metric are presented. This paper provides the possible future research direction of FA.
... Subramanian et al. [57] modified α as α = αmax − Itr(αmax−α min ) ...
... Both Attraction and Random Movement α, γ in every iteration according to location and maximum power per location, and β = 1 [57] Both Attraction and Random Movement ...
A comprehensive review of firefly algorithm is presented and various characteristics are discussed with various variant of FA such as binary, multi-objective and hybrid.
... Subramanian et al. [57] modified α as α = αmax − Itr(αmax−α min ) ...
... Both Attraction and Random Movement α, γ in every iteration according to location and maximum power per location, and β = 1 [57] Both Attraction and Random Movement ...
A comprehensive review of firefly algorithm is presented and various characteristics are discussed with various variant of FA such as binary, multi-objective and hybrid.
... The function of the objective is defined to minimize the generation cost total of thermal plants with optimal generation power for equality and inequality constraints [4]. ISSN: 2005-4238 IJAST Copyright ⓒ 2020 SERSC Some previous studies related to optimization methods for economic dispatch problems such as chaotic PSO [5], evolutionary programming techniques [6], multiobjective differential evolution with two objective functions [7], evolutionary algorithms [8], improved local search involving bee colony optimization using lambda iteration combined with a golden section search method [9], an efficient meta heuristic algorithm [10], multiobjective fruit fly optimization algorithm with three objective functions [11], firefly algorithm [12], multi-objective harmony search algorithm with two objective functions [13], fuzzified artificial bee colony algorithm with three objective functions [14], a novel method with across neighborhood search [15], MO-FOA with two objective functions [16], simulated annealing algorithm [17], assessment of hurricane versus sine-cosine optimization with two objective functions [18], flower pollination algorithm with two objective functions [19], a chaotic krill herd algorithm with one objective function [20], hybrid with cross-entropy method and sequential quadratic programming with one objective function [21], variable scaling hybrid differential evolution with one objective function [22], hybrid particle multi-swarm optimization with one objective function [23], hybrid PSO-TLBO optimization technique with one objective function [24], new hybrid ICA-PSO approach with one objective function [25], novel hybrid differential evolution algorithm with one objective function [26]. ...
A large electric power system requires an interconnection process to maintain the continuity of electricity service and lighten the burden of the existing power station in the system. In the electrical energy distribution process of the thermal power plant to the load center, a minimum operation cost is required. In this paper, the FOA-ABC hybrid optimization method is proposed to get the best solution from multiobjective dynamic economic dispatch (MDED) in an electrical power system for five tested objective functions by reviewing the power balanced, power limits of the generator, and the generator ramp rate as constraints. The test is done by using three different electrical systems, namely thermal-wind integration of the 26 bus IEEE system that was carried out in two test stages with two functions of objective, the 30 bus IEEE thermal power plant system that also is carried out in two test stages with three and five objective functions, and the Sulselbar thermal power plant real system in Indonesia with two objective functions. Simulation results of the FOA-ABC hybrid optimization method for thermal-wind integration of the 26 bus IEEE system, the 30 bus IEEE system, and the Sulselbar thermal power plant real system compared with the existing another method. It showed that the FOA-ABC hybrid optimization method able to solve multiobjective dynamic economic dispatch problems better with smaller value than the other methods that are being compared. Besides that, the wind power plant integration into thermal power plants can reduce the total operating costs of power plants in an interconnected system.
