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.

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Abstract: Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed “continuous orthogonal ant colony” (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an “adaptive regional radius” method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization — API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

Iteration particle swarm optimization procedure for economic load dispatch with generator constraints

Abstract: In this paper, iteration particle swarm optimization (IPSO) has been applied to determine the feasible optimal solution of the economic load dispatch (ELD) problem considering various generator constraints. Many realistic constraints, such as ramp rate limits, generation limitation, prohibited operating zone, transmission loss and nonlinear cost functions are all considered for practical operation. The performance of the classical particle swarm optimization (CPSO) greatly depends on its parameters, and it often suffers the problem of being trapped in local optima. A new index named, Iteration Best, is incorporated in CPSO to enrich the searching behavior, solution quality and to avoid being trapped into local optimum. Two test power systems, including 6 and 15 unit generating, are applied to compare the performance of the proposed algorithm with PSO, chaotic PSO, hybrid GAPSO, self organizing hierarchical PSO (SOH_PSO) methods. The numerical results affirmed the robustness and proficiency of proposed approach over other existing methods.

An empirical study of multifactorial PSO and multifactorial DE

Abstract: Recently, the notion of Multifactorial Optimization (MFO) has emerged as a promising approach for evolutionary multi-tasking by automatically exploiting the latent synergies between optimization problems, simply through solving them together in an unified representation space [1]. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge between them. In [1], the efficacy of MFO has been studied by a specific mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. Here we further explore the generality of MFO when diverse population based search mechanisms are employed. In particular, in this paper, we present the first attempt to conduct MFO with the popular particle swarm optimization and differential evolution search. Two specific multi-tasking paradigms, namely multifactorial particle swarm optimization (MFPSO) and multifactorial differential evolution (MFDE) are proposed. To evaluate the performance of MFPSO and MFDE, comprehensive empirical studies on 9 single objective MFO benchmark problems are provided.

Dynamic multi-swarm particle swarm optimizer with harmony search

Abstract: In this paper, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) is improved by hybridizing it with the harmony search (HS) algorithm and the resulting algorithm is abbreviated as DMS-PSO-HS. We present a novel approach to merge the HS algorithm into each sub-swarm of the DMS-PSO. Combining the exploration capabilities of the DMS-PSO and the stochastic exploitation of the HS, the DMS-PSO-HS is developed. The whole DMS-PSO population is divided into a large number of small and dynamic sub-swarms which are also individual HS populations. These sub-swarms are regrouped frequently and information is exchanged among the particles in the whole swarm. The DMS-PSO-HS demonstrates improved on multimodal and composition test problems when compared with the DMS-PSO and the HS.