An optimization algorithm for multimodal functions inspired by collective animal behavior

TL;DR

This paper introduces the Collective Animal Behavior (CAB) algorithm, inspired by animal group behaviors, to efficiently locate multiple optima in complex multimodal functions, outperforming existing methods.

Contribution

The paper proposes a novel optimization algorithm based on biological collective behaviors, demonstrating improved performance on benchmark multimodal problems.

Findings

CAB effectively finds multiple optima with higher efficiency.

Experimental results outperform existing multimodal optimization methods.

The algorithm models biological collective motion for optimization tasks.

Abstract

Interest in multimodal function optimization is expanding rapidly since real world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the Collective Animal Behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact to each other based on the biological laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison to other methods reported in the literature.