Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models

TL;DR

This paper introduces an adaptive surrogate model framework to enhance cooperative coevolution for large-scale optimization, significantly reducing computational costs while maintaining high solution quality.

Contribution

It proposes a novel adaptive surrogate modeling approach tailored for different sub-problems in cooperative coevolution, improving efficiency and solution accuracy.

Findings

Achieves better solutions than traditional CC algorithms.

Reduces computational resources needed for evaluation.

Effective on IEEE CEC 2010 benchmark functions.

Abstract

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each sub-solution and thus requires many computation resources. To alleviate this issue, this study proposes an adaptive surrogate model assisted CC framework. This framework adaptively constructs surrogate models for different sub-problems by fully considering their characteristics. For the single dimensional sub-problems obtained through decomposition, accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding sub-problems directly. As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models. By these means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional CC algorithms and a non-CC algorithm even with much fewer computation resources.