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This paper presents a multiobjective analog/RF circuit sizing tool using an improved brain storm optimization (IMBSO) algorithm with the purpose of analyzing the tradeoffs between competing performance specifications of analog/RF circuit block. A number of improvements are incorporated into IMBSO algorithm at different steps. At first, the clusteri...
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Citations
... In 2016, Chen et al. [29] proposed an improved affinity propagation (AP) clustering method and an enhanced creation strategy for the structural information of single or multiple clusters to adaptively change the number of clusters during the search process. In 2018, Dash et al. [31] introduced k-means++ technology to improve the BSO algorithm, which solves the problem of the slow convergence of the algorithm by using a random probability decision in the river formation dynamics scheme to select the best clustering centroid for population generation. In 2018, Duan et al. [32] introduced a new clustering method based on metric distance into the basic BSO, proposed metric distance brainstorm optimization (MDBSO), and applied the improved algorithm. ...
The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this article, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this article consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.
... A grid-based method and a hybrid mutation strategy integrating above traditional Gaussian-, Cauchy-and Chaotic-based mutation replace k-means clustering and Gaussian mutation in [50] to enhance the diversity and avoiding the premature convergence. A random probabilistic decision-making of river formation dynamics scheme to select optimal cluster centroids during population generation step, and an adaptive mutation operator were taken in [51] to improve the performance of IMBSO. A multi-objective brainstorming algorithm based on multiple indicators (MIBSO) is proposed in [52] to extend BSO algorithms. ...
In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving many-objective optimization problems. Inspired by the human brainstorming conference, Brain Storming Optimization (BSO) algorithm was guided by the cluster centers and other individuals with probability, which can balance convergence and diversity greatly. In this paper, the authors propose a novel brain storm optimization algorithm for many-objective optimization problem. The algorithm adopts the decision variable clustering method to divides the variables into convergence-related variables and diversity-related variables. The decomposition strategy is designed to increases selection pressure for the convergence-related variables, while the reference point’s strategy is adopted for the diversity-related variables to update the population and increase the diversity. Experimental results show that the proposed many-objective brain storm optimization algorithm is a very promising algorithm for solving many-objective optimization problems.
... Brain storm optimization (BSO) is a new swarm intelligence algorithm which is proposed by simulating the collective behavior of human being [29][30][31]. Up to now, BSO has been successfully applied to many real problems, such as system identification problem [32], wind speed forecasting [33], analog/RF circuit sizing optimization [34], Loney's solenoid problem [35], fault section diagnosis [36], and so on. Experiment results in those literatures showed the effectiveness of BSO. ...
Feature selection is an important preprocessing technique for data. Brain storm optimization (BSO) is one of the latest swarm intelligence algorithms, which simulates the collective behavior of human beings. However, traditional updating mechanisms in BSO limit its application in feature selection. We study a new individual clustering technology and two individual updating mechanisms in BSO for developing novel feature selection algorithms with the purpose of maximizing the classification performance. The proposed individual updating mechanisms are compared with each other. The more promising updating mechanism and the new individual clustering technology are combined into the BSO framework to form a new wrapper feature selection algorithm, called BBSOFS. Compared with existing algorithms including particle swarm optimization, firefly algorithm and BSO algorithm, experimental results on benchmark datasets show that with the help of the proposed individual clustering and updating mechanism, the proposed BBSOFS algorithm can obtain feature subsets with good classification accuracy.
... initialize drops initialize nodes repeat move drops erode paths deposit sediments analyze paths until stopping criteria met RFD has been used by different research groups to solve a variety of problems. In addition to well-known academic NP-hard problems like the traveling salesman or Steiner tree problems [19,20], it has also been used in more specific industrial domains, like monitoring electrical power systems [21] or designing VLSI circuits [22,23]. In particular, due to its intrinsic absence of cycles, it has been used to design several routing protocols in computer networks (see, e.g., [24,25]), to test systems [26], or to improve robot navigation [27]. ...
Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences.
Recently, Electronic Design Automation (EDA) tools
have been more pronounced during the design of analog
integrated circuits (ICs) to ease the complicated design process
of highly nonlinear design problems. Conventionally, meta�heuristic algorithms have been employed as the core of the
optimization engine, where Evolutionary Algorithms (EAs) are
commonly preferred due to their high accuracy and efficiency
for not only single- but also multi- and many-objective design
problems. Although numerous successful derivations of the
EAs have been proposed in the literature, the search for a
better algorithm that fits the analog/RF IC sizing problem
still pursuits. In this paper, one of the most promising EA�based approach, namely Two-Archive Evolutionary Algorithm
(TAEA), is applied to the analog IC sizing problem at multi�and many objective domains. To evaluate the performance of the
TAEA, two analog circuits have been synthesized and the results
have been compared with the results obtained by Non-Sorting
Genetic Algorithm-II (NSGAII) and Multi-Objective Evolutionary
Algorithm based Decomposition (MOEA/D) through the well�known Pareto-optimal front quality metrics
Thanks to the enhanced computational capacity of modern computers, even sophisticated analog/RF circuit sizing problems can be solved via electronic design automation (EDA) tools. Recently, several analog/RF circuit optimization algorithms have been successfully applied to automatize the analog/RF circuit design process. Conventionally, metaheuristic algorithms are widely used in optimization process. Among various nature-inspired algorithms, evolutionary algorithms (EAs) have been more preferred due to their superiorities (robustness, efficiency, accuracy etc.) over the other algorithms. Furthermore, EAs have been diversified and several distinguished analog/RF circuit optimization approaches for single-, multi-, and many- objective problems have been reported in the literature. However, there are conflicting claims on the performance of these algorithms and no objective performance comparison has been revealed yet. In the previous work, only a few case study circuits have been under test to demonstrate the superiority of the utilized algorithm, so a limited comparison has been made for only these specific circuits. The underlying reason is that the literature lacks a generic benchmark for analog/RF circuit sizing problem. To address these issues, we propose a comprehensive comparison of the most popular two evolutionary computation algorithms, namely Non-Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Evolutionary Algorithm based Decomposition (MOEA/D), in this paper. For that purpose, we introduce two ad-hoc testbenches for analog (ANLG) and radio frequency (RF) circuits including the common building blocks. The comparison has been made at both multi- and many- objective domains and the performances of algorithms have been quantitatively revealed through the well-known Pareto-optimal front quality metrics.
Many cities around the world face the flooding management problems as extreme rainfall events have become more frequent. It’s of great practical significance to design an effective urban drainage system (UDS) to improve the stormwater runoff quality. An integrated decision-making analysis framework based on many-objective brain storm optimization is proposed to make the optimal design of UDS. Firstly, a generic mathematical model is presented considering five objectives simultaneously. Secondly, an effective many-objective brain storm optimization based on local region dominance (DRP) is designed as the solver of the model and a set of Pareto-optimal solutions are obtained. Thirdly, two kinds of multi-attribute decision analysis methods are introduced to rank the Pareto-optimal solutions. Fourthly, the comparison experiments of a case study with three multi-objective evolutionary algorithms show the promising performance of the integrated decision-making framework and the corresponding optimization algorithm.
