An integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems

Multi-objective optimization problems often load in the multi-scenario environment, and they can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far, existing research on MSMOs mainly focuses on the specific method in engineering applications, and general methods are quite scarce. This paper develops an integrated information-based evolutionary framework to find a set of scenario-based non dominated solutions for MSMOs. Sequentially, a scenario-based dominance principle evolutionary algorithm and a decomposition-based evolutionary algorithm are combined with the framework to construct two integration algorithms, respectively. Furthermore, the crucial components in both algorithms are developed under the multi-scenario environment, such as scenario-based constraint handling, scenario-based dominance concept, scenario based crowded degree and multi-scenario decomposition method. Additionally, we design a novel comprehensive evaluation indicator for MSMOs. Finally, the proposed framework is tested on numerous artificial and engineering problems. Experiments demonstrate that the framework has superiority over four existing state-of-the-art peer competitors concerning the indicator. Two integration algorithms are able to find a set of good scenario-based non dominated solutions.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper develops an integrated information-based evolutionary framework for solving multi-scenario multi-objective optimization problems. Two integration algorithms, scenario-based dominance principle evolutionary algorithm and decomposition-based evolutionary algorithm, are constructed using the framework, and crucial components are developed under the multi-scenario environment.