Multiple Global Peaks Big Bang-Big Crunch Algorithm for Multimodal Optimization

TL;DR

The paper introduces MGP-BBBC, a novel multimodal optimization algorithm that enhances the Big Bang-Big Crunch method with clustering and filtering techniques to effectively identify multiple peaks in complex landscapes.

Contribution

It proposes a specialized multi-peak optimization algorithm that improves peak detection accuracy by integrating clustering, filtering, and balanced exploration-exploitation strategies.

Findings

MGP-BBBC outperforms or matches state-of-the-art methods on benchmark functions.

The algorithm effectively identifies multiple peaks in complex multimodal landscapes.

Experimental results demonstrate its robustness and efficiency.

Abstract

The main challenge of multimodal optimization problems is identifying multiple peaks with high accuracy in multidimensional search spaces with irregular landscapes. This work proposes the Multiple Global Peaks Big Bang-Big Crunch (MGP-BBBC) algorithm, which addresses the challenge of multimodal optimization problems by introducing a specialized mechanism for each operator. The algorithm expands the Big Bang-Big Crunch algorithm, a state-of-the-art metaheuristic inspired by the universe's evolution. Specifically, MGP-BBBC groups the best individuals of the population into cluster-based centers of mass and then expands them with a progressively lower disturbance to guarantee convergence. During this process, it (i) applies a distance-based filtering to remove unnecessary elites such that the ones on smaller peaks are not lost, (ii) promotes isolated individuals based on their niche count after clustering, and (iii) balances exploration and exploitation during offspring generation to target specific accuracy levels. Experimental results on twenty multimodal benchmark test functions show that MGP-BBBC generally performs better or competitively with respect to other state-of-the-art multimodal optimizers.