Topic
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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TL;DR: In this paper, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models.
Abstract: There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. These are iterative search processes that efficiently perform the exploration and exploitation in the solution space, aiming to efficiently find near optimal solutions. Considering the solution space in a specified region, some models contain global optimum and multiple local optima. In this context, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by fireflies behavior in nature. PSO is population based optimization technique inspired by social behavior of bird flocking or fish schooling. A series of computational experiments using each algorithm were conducted. The results of this experiment were analyzed and compared to the best solutions found so far on the basis of mean of execution time to converge to the optimum. The Firefly algorithm seems to perform better for higher levels of noise.
185 citations
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TL;DR: A new hybrid swarm intelligence algorithm consisting of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem.
Abstract: The job-shop scheduling problem has attracted many researchers' attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem. The experimental results show that the new proposed job-shop scheduling algorithm is more robust and efficient than the existing algorithms.
185 citations
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11 Sep 2006TL;DR: Results shown that the addition of a mutation operator to PSO can enhance optimisation performance and insight is gained into how to design mutation operators dependent on the nature of the problem being optimized.
Abstract: The Particle Swarm Optimization (PSO) technique can be augmented with an additional mutation operator that helps prevent premature convergence on local optima. In this paper, different mutation operators for PSO are empirically investigated and compared. A review of previous mutation approaches is given and key factors concerning how mutation operators can be applied to PSO are identified. A PSO algorithm incorporating different mutation operators is applied to both mathematical and constrained optimization problems. Results shown that the addition of a mutation operator to PSO can enhance optimisation performance and insight is gained into how to design mutation operators dependent on the nature of the problem being optimized.
184 citations
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TL;DR: This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences.
Abstract: Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences. This optimization procedure is used to design the reference governor for the control system. This approach provides the means to specify optimal set points for controllers under a diversity of operating scenarios. Variations of the PSO technique, hybrid PSO, evolutionary PSO, and constriction factor approach are applied to the FFPU, and the comparison is made among the PSO techniques and genetic algorithm.
184 citations
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TL;DR: Compared to the basic Binary Particle Swarm Optimization (BPSO), this improved algorithm introduces a new probability function which maintains the diversity in the swarm and makes it more explorative, effective and efficient in solving KPs.
184 citations