Uncertainty And Evolutionary Optimization: A Novel Approach

TL;DR

This paper introduces DPSEA, a distributed population switching evolutionary algorithm that effectively handles noisy fitness evaluations in complex optimization problems, demonstrating superior robustness and accuracy over existing methods.

Contribution

The paper proposes a novel distributed population switching architecture with local regression for fitness estimation, improving optimization in noisy environments.

Findings

DPSEA outperforms existing methods on benchmark problems.

The approach enhances robustness and accuracy in noisy optimization.

Distributed self-adaptive memory improves solution quality.

Abstract

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.