Triple Archives Particle Swarm Optimization

Abstract

There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.

Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

“…• PSO has faster execution speed and higher efficiency of problem-solving. Based on this feature, [13] proposes a triple archives PSO (TAPSO) and [14] proposes an eXpanded PSO (XPSO). • For all particles in PSO, they can search solutions concurrently, which can be executed in parallel.…”

Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

“…Usually, a kind of swarm intelligence algorithm has many variants, which have different search strategies or parameters. Take the PSO algorithm as an example; for single-objective optimization, there are adaptive PSO algorithm [8], time-varying attractor in PSO algorithm [9], interswarm interactive learning strategy in PSO algorithm [10], triple archives PSO algorithm [11], social learning PSO algorithm for scalable optimization [12], PSO variant for mixed-variable optimization problems [13], etc. For multiobjective optimization, there are adaptive gradient multiobjective PSO algorithm [14], coevolutionary PSO algorithm with bottleneck objective learning strategy [15], normalized ranking based PSO algorithm for many-OB-JECTIVE optimization [16], etc.…”

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

“…Liu et al proposed a coevolutionary particle swarm optimization with a bottleneck objective learning (BOL) strategy for many-objective optimization (CPSO) [32]. Xia et al proposed a triple archives PSO (TAPSO) [33]. Although the convergence accuracy of these algorithms is higher compared with the basic PSO, their convergence and robustness are still unsatisfactory.…”