Particle Swarm Optimization with Multiple Adaptive Subswarms

Abstract

Benefiting from its simplicity and efficiency, particle swarm optimization (PSO) algorithm has shown great performance on various problems. However, for different optimization problems or different search areas, it is still difficult to achieve a satisfying trade-off between exploration and exploitation. On the basis of canonical PSO algorithm, a variety of improved algorithms have been proposed, which have different capabilities of exploitation and exploration, and each of them performs effective in some problems. This paper proposes a particle swarm optimization with multiple adaptive sub-swarms (PSOMAS). It uses multiple subswarms strategy, in which each sub-swarm is evolved by different algorithms, and an adaptive strategy is also used to reduce the consumption of computing resources. A comprehensive experimental study is conducted on 30 benchmark functions, to compare with several well-known variants of PSO algorithms. The results show that PSOMAS with RT=100 could obtain a better overall performance than all others. Moreover, PSOMAS could find high-quality solution in different problems by varying the value of RT.

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

“…This algorithm, however, uses the variances and average fitness values of dynamic sub-swarms to measure the distribution of the particles, in order to detect the dominant and the optimal particle. g) PSOMAS [26]: In the Particle Swarm Optimization with Multiple Adaptive Sub-swarms (PSOMAS) algorithm, each sub-swarm is evolved by a completely different variant of the single swarm PSO algorithm, such as Comprehensive Learning PSO [36] and Cooperative PSO [37]. This work also uses an adaptive strategy to reduce usage of computational resources.…”

Dynamic immune cooperative scheduling of agricultural machineries

“…The multi-objective optimization algorithm (MOA) has been a hot issue in recent years because of its balance on each aspects of the problem, which mainly consists of the particle swarm optimization (PSO) algorithms [1][2][3], the immune clonal algorithms (ICA) [4], the evolutionary algorithms (EA) [5][6][7], the differential evolution (DE) algorithms [8,9], and other hybrid heuristic algorithms [10][11][12][13][14]. These biological heuristics algorithms obtain better solutions through a continuous iterative process,in which a set of search rules are proposed.…”