Mechanism design and followability evaluation for Swarm Intelligence Algorithms toward continuous changes on 2 types of axis

Abstract

This paper focuses on swarm intelligence (SI) algorithms to tackle the dynamic optimization problems (DOPs), and aims at investigating the effectiveness of the proposed mechanisms by incorporating them with the conventional SI algorithms. For this purpose, this paper starts to divide DOPs into the two types, “sudden change” where an optimal solution changes one time and “continuous change” where an optimal solution changes over time, and addresses the latter change which is more difficult than the former change. In detail, this paper explores the mechanisms for “the solution change on the evaluation value axis” where the local solution change to the optimal solution and vice versa and for “the solution change on the design variable axis” where the optimal solution moves gradually in search space. To tackle these solution changes in continuous change, this thesis proposes the mechanism for the former solution change (called as the Adaptive Local Information Sharing (ALIS) mechanism which tracks the solution change by limiting the search range) and the mechanism for the latter solution change (called as the Jumping Over toward Future Best (JOFB) mechanism which explores the search area by estimating the moving direction and range of the future optimal solution). For the intensive experiments of the proposed mechanisms on the various functions which solution landscape changes over time, the proposed mechanisms are incorporated to three SI algorithms (Partical Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Social Spider Optimization (SSO)) and the following implications have been revealed: (1) Algorithms incorporated ALIS mechanism (PSO-ALIS, ABC-ALIS, SSO-ALIS) can track the optimal solution change on the evaluation value axis by capturing the multiple local solutions simultaneously; and (2) Algorithms incorporated JOFB mechanism (PSO-JOFB, ABC-JOFB, SSO-JOFB) can track the optimal solution change on the design variable axis by searching the direction and range of the future optimal solution in advance; (3) ABC, PSO and SSO with ALIS and JOFB mechanishm can track to “continuous change” with two axial changes.