Efficient multi-swarm PSO algorithms for dynamic environments

Abstract

Particle swarm optimization has been successfully applied in many research and application areas because of its effectiveness and easy implementation. In this work we extend one of its variants to address multi-modal dynamic optimization problems, the multi-swarm PSO (mPSO) proposed by Blackwell and Branke. The aim of our proposal is to increase the efficiency of this algorithm. To this end, we propose techniques operating at swarm level: one of which divides each swarm into two groups depending on the quality of the particles for facing the loss of diversity, and the other control the number of active swarms during the run using a fuzzy rule. A detailed experimental analysis shows the robustness of our proposal.

A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

“…[28] at the swarm degree. Similar strategies were also used by the other authors for controlling the number of active swarms during the run [29,37]. The major difference between the proposed mechanism in this work and those introduced in mentioned papers is that in this work we apply the hibernation at individual degree, which is one of the contributions of this paper.…”

Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

“…Numerous real-world scenarios that can be modelled as dynamic optimization problems (DOPs) are characterized by the dynamic nature of the model elements, for example: the objective function or search space. Solving DOPs by metaheuristics has been productive [23][24][25] because of their capacity to deal with complex scenarios, and incorporation of specific mechanisms to face problem dynamics. Current mechanisms for DOPs can be categorized as: diversity during program run, diversity after changes, memory approaches, and multi-population approaches [26].…”

Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments

“…Novoa-Hernández et al [39] removed the quantum particle component of the MPSO algorithm [6]. The authors introduced a diversity increasing scheme directly after a change in the environment by replacing the worst-performing particles within each sub-swarm by particles that are randomly created in a hypersphere centred at the best particle in the sub-swarm.…”

“…MPSOD: [39] MPSOD is compared to ABrCDE using the Det n k −local detection strategy. Overlapping confidence intervals were found for a change severity of 1 and 2.…”