Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems

Abstract

In many real-world decision problems, there are specific objective, decision variables, conditions, data and/or parameters that may vary over time. When these problems are solved through an optimization process, they are generally known as dynamic optimization. Dynamic optimization is a challenging research topic as the optimal solution usually moves with any change in the problem environment. In locating the optimal point in any optimization problems, the effectiveness of the search process is influenced by the nature of the problem landscape. In order to improve the effectiveness of the search process, in this article, a new approach is developed by integrating a landscape-based strategy with appropriately designed evolutionary algorithms for solving dynamic problems. The proposed approach is named as Landscape Influenced Dynamic Optimization Algorithm (LIDOA). LIDOA checks the similarity before and after the changed environment which is then used as an input to guide the evolutionary search process. The dynamic benchmark functions from IEEE-CEC2009 are solved using LIDOA. LIDOA was able to enhance the performance of the evolutionary algorithms in their adaptation to dynamic changes, which in turn enhanced their ability to attain better results.

Keywords

• Fitness landscape
• dynamic optimization
• similarity check