Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems

TL;DR

This paper introduces an Attention-oriented Brain Storm Optimization (ABSO) method that uses an attention mechanism to effectively locate multiple global and local optima in multimodal optimization problems, reducing the need for prior knowledge.

Contribution

The paper proposes a novel ABSO algorithm that integrates attention mechanisms into BSO to improve multimodal optimization by automatically identifying multiple solutions.

Findings

Successfully locates multiple global and local optima on benchmark functions.

Requires less prior problem knowledge compared to existing methods.

Demonstrates potential for further development in multimodal optimization.

Abstract

Population-based methods are often used to solve multimodal optimization problems. By combining niching or clustering strategy, the state-of-the-art approaches generally divide the population into several subpopulations to find multiple solutions for a problem at hand. However, these methods only guided by the fitness value during iterations, which are suffering from determining the number of subpopulations, i.e., the number of niche areas or clusters. To compensate for this drawback, this paper presents an Attention-oriented Brain Storm Optimization (ABSO) method that introduces the attention mechanism into a relatively new swarm intelligence algorithm, i.e., Brain Storm Optimization (BSO). By converting the objective space from the fitness space into "attention" space, the individuals are clustered and updated iteratively according to their salient values. Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions. The preliminary results show that the proposed method can locate multiple global and local optimal solutions of several multimodal benchmark functions. The proposed method needs less prior knowledge of the problem and can automatically converge to multiple optimums guided by the attention mechanism, which has excellent potential for further development.