Abstract
Although particle swarm optimization (PSO) is a powerful evolutionary algorithm for solving nonlinear optimization problems in deterministic environments, many practical problems have some stochastic noise. The optimal computing budget allocation (OCBA) has been integrated into PSO in various ways to cope with this. The OCBA can mitigate the effect of noise on PSO by selecting the best solution under a limited evaluation budget. Recently, with the increasing complexity of PSO applications, the evaluation costs are also increasing rapidly, which has sparked the need for more efficient PSO in stochastic environments. This article proposes a simple yet effective adjustment to the integration of OCBA to further improve the efficiency of PSO. The proposed adjustment allows OCBA to expand its search space to find the global best position more correctly such that the entire swarm can move on a better direction under stochastic noise. The experimental results on various benchmarks demonstrate the improved performance of PSO by the proposed adjustment under a limited budget compared with the latest studies. In addition, the results regarding fighters’ evasion flight optimization emphasize the practical need for the proposed adjustment.
Highlights
Inspired by the social behavior of swarm, particle swarm optimization (PSO) [1] is a population-based evolutionary algorithm widely used for solving complex optimization problems [2] or learning artificial neural networks [3]
The section proposes a simple yet effective adjustment to the integration of optimal computing budget allocation (OCBA) to further improve the efficiency of PSO in stochastic environments
This article proposed a simple yet effective adjustment to the integration of OCBA to further improve the efficiency of PSO in stochastic environments
Summary
Inspired by the social behavior of swarm, particle swarm optimization (PSO) [1] is a population-based evolutionary algorithm widely used for solving complex optimization problems [2] or learning artificial neural networks [3]. W. Bae: Effective Adjustment to the Integration of OCBA for PSO in Stochastic Environments for the noisy fitness values of neighboring particles, as the neighborhood’s solutions become similar as PSO iterates. The resampling approach combined ranking and selection (R&S) methods, such as the indifference-zone (IZ) [9], the optimal computing budget allocation (OCBA) [10], and the uncertainty evaluation (UE) [11], into PSO for efficient sample allocations These R&S methods intelligently allocate a limited number of samples to correctly select the best solution from a finite set of alternatives. The proposed adjustment allows OCBA to select the Gbest solution more correctly, thereby mitigating the effect of noise and increasing the performance of PSO under a limited evaluation budget.
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