Exploration Enhancement of Nature-Inspired Swarm-based Optimization Algorithms

TL;DR

This paper introduces a Perturbation-Projection strategy to improve the exploration ability of swarm-based optimization algorithms, ensuring convergence to a global optimum and outperforming original methods in various tests.

Contribution

It proposes a novel Perturbation-Projection strategy and provides convergence conditions, enhancing the exploration and global optimization performance of swarm algorithms.

Findings

Enhanced algorithms outperform original versions in numerical experiments.

Convergence to global optimum is guaranteed with the proposed strategy.

The approach is demonstrated on PSO, BAT, and CSO algorithms.

Abstract

Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and assumption-free. A common drawback of these algorithms is premature convergence and the solution found is not a global optimum. We provide sufficient conditions for an algorithm to converge almost surely (a.s.) to a global optimum. We then propose a general, simple and effective strategy, called Perturbation-Projection (PP), to enhance an algorithm's exploration capability so that our convergence conditions are guaranteed to hold. We illustrate this approach using three widely used nature-inspired swarm-based optimization algorithms: particle swarm optimization (PSO), bat algorithm (BAT) and competitive swarm optimizer (CSO). Extensive numerical experiments show that each of the three algorithms with the enhanced PP strategy outperforms the original version in a number of notable ways.