Dynamic multi-objective optimization using evolutionary algorithms: a survey

R Azzouz, S Bechikh, L Ben Said - Recent advances in evolutionary multi …, 2017 - Springer
Recent advances in evolutionary multi-objective optimization, 2017Springer
Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although
dynamic optimization and multi-objective optimization have separately obtained a great
interest among many researchers, there are only few studies that have been developed to
solve Dynamic Multi-objective Optimisation Problems (DMOPs). Moreover, applying
Evolutionary Algorithms (EAs) to solve this category of problems is not yet highly explored …
Abstract
Dynamic Multi-objective Optimization is a challenging research topic since the objective functions, constraints, and problem parameters may change over time. Although dynamic optimization and multi-objective optimization have separately obtained a great interest among many researchers, there are only few studies that have been developed to solve Dynamic Multi-objective Optimisation Problems (DMOPs). Moreover, applying Evolutionary Algorithms (EAs) to solve this category of problems is not yet highly explored although this kind of problems is of significant importance in practice. This paper is devoted to briefly survey EAs that were proposed in the literature to handle DMOPs. In addition, an overview of the most commonly used test functions, performance measures and statistical tests is presented. Actual challenges and future research directions are also discussed.
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