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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


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Journal ArticleDOI
TL;DR: Application of the proposed algorithm on some benchmark functions demonstrated its good capability in comparison with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and the results of the experiments showed the good performance of FOA in some data sets from the UCI repository.
Abstract: In this article, a new evolutionary algorithm, Forest Optimization Algorithm (FOA), suitable for continuous nonlinear optimization problems has been proposed. It is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period. In FOA, seeding procedure of the trees is simulated so that, some seeds fall just under the trees, while others are distributed in wide areas by natural procedures and the animals that feed on the seeds or fruits. Application of the proposed algorithm on some benchmark functions demonstrated its good capability in comparison with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Also we tested the performance of FOA on feature weighting as a real optimization problem and the results of the experiments showed the good performance of FOA in some data sets from the UCI repository.

166 citations

Journal ArticleDOI
TL;DR: The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.
Abstract: The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.

166 citations

01 Jan 2001
TL;DR: The performance of the recently proposed Particle Swarm optimization method in the presence of noisy and continuously changing environments is studied.
Abstract: In this paper we study the performance of the recently proposed Particle Swarm optimization method in the presence of noisy and continuously changing environments. Experimental results for well known and widely used optimization test functions are given and discussed. Conclusions for its ability to cope with such environments as well as real– life applications are also derived.

165 citations

Journal ArticleDOI
TL;DR: A Fuzzy Self Adaptive Particle Swarm Optimization (FSAPSO) algorithm is proposed and implemented to dispatch the generations in a typical micro-grid considering economy and emission as competitive objectives.
Abstract: Nowadays, it becomes the head of concern for many modern power girds and energy management systems to derive an optimal operational planning with regard to energy costs minimization, pollutant emissions reduction and better utilization of renewable resources of energy such as wind and solar. Considering all the above objectives in a unified problem provides the desired optimal solution. In this paper, a Fuzzy Self Adaptive Particle Swarm Optimization (FSAPSO) algorithm is proposed and implemented to dispatch the generations in a typical micro-grid considering economy and emission as competitive objectives. The problem is formulated as a nonlinear constraint multi-objective optimization problem with different equality and inequality constraints to minimize the total operating cost of the micro-grid considering environmental issues at the same time. The superior performance of the proposed algorithm is shown in comparison with those of other evolutionary optimization methods such as conventional PSO and genetic algorithm (GA) and its efficiency is verified over the test cases consequently.

165 citations

Journal ArticleDOI
14 Sep 1997
TL;DR: An approximate optimization strategy is developed in which a cumulative response surface approximation of the augmented Lagrangian is sequentially optimized subject to a trust region constraint to show that the optimization process converges to a solution of the original problem.
Abstract: A common engineering practice is the use of approximation models in place of expensive computer simulations to drive a multidisciplinary design process based on nonlinear programming techniques. The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem. To date the primary focus of most approximate optimization strategies is that application of the method should lead to improved designs. This is a laudable attribute and certainly relevant for practicing designers. However to date few researchers have focused on the development of approximate optimization strategies that are assured of converging to a solution of the original problem. Recent works based on trust region model management strategies have shown promise in managing convergence in unconstrained approximate minimization. In this research we extend these well established notions from the literature on trust-region methods to manage the convergence of the more general approximate optimization problem where equality, inequality and variable bound constraints are present. The primary concern addressed in this study is how to manage the interaction between the optimization and the fidelity of the approximation models to ensure that the process converges to a solution of the original constrained design problem. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, one can show that the optimization process converges to a solution of the original problem. In this research an approximate optimization strategy is developed in which a cumulative response surface approximation of the augmented Lagrangian is sequentially optimized subject to a trust region constraint. Results for several test problems are presented in which convergence to a Karush-Kuhn-Tucker (KKT) point is observed.

165 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171