Multi-Cohort Intelligence Algorithm: An Intra- and Inter-group Learning Behavior based Socio-inspired Optimization Methodology

TL;DR

This paper introduces a Multi-Cohort Intelligence metaheuristic that leverages intra- and inter-group learning to improve optimization performance, validated on multiple test problems against several state-of-the-art algorithms.

Contribution

It presents a novel socio-inspired optimization algorithm with intra- and inter-group learning mechanisms, outperforming existing algorithms in solution quality and computational efficiency.

Findings

Multi-CI outperforms recent algorithms in solution quality.

The algorithm reduces computational time and evaluations.

Statistical tests confirm the significance of improvements.

Abstract

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimization domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimization, Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-adaptive differential evolution algorithm, Comprehensive Learning Particle Swarm Optimization, Backtracking Search Optimization Algorithm and Ideology Algorithm. The Wilcoxon signed rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations are discussed as well. In addition, an illustrative example is also solved and every detail is provided.