A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization

TL;DR

This paper introduces a cumulative multi-niching genetic algorithm that efficiently explores multimodal functions by utilizing all evaluations and controlling population density, leading to faster convergence and fewer evaluations.

Contribution

The paper proposes a novel cumulative multi-niching genetic algorithm that improves convergence speed and reduces function evaluations for multimodal optimization problems.

Findings

Outperforms three other multi-niching algorithms in convergence speed.

Achieves an order-of-magnitude reduction in function evaluations.

Provides robust convergence to multiple local optima.

Abstract

This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control over the design space reduces unnecessary objective function evaluations. The algorithm's novel arrangement of genetic operations provides fast and robust convergence to multiple local optima. Benchmark tests alongside three other multi-niching algorithms show that the CMN GA has a greater convergence ability and provides an order-of-magnitude reduction in the number of objective function evaluations required to achieve a given level of convergence.