Genetic Algorithm Based on Explicit Memory for Solving Dynamic Problems

Nowadays, it is common to find optimal point of the dynamic problem; dynamic problems whose optimal point changes over time require algorithms which dynamically adapt the search space instability. In the most of them, the exploitation of some information from the past allows to quickly adapt after an environmental change (some optimal points change). This is the idea underlining the use of memory in the field, which involves key design issues concerning the memory content, the process of memory update, and the process of memory retrieval. With use of the Aging Best Solution and Keeping Diversity in Population, the speed convergence of algorithm can be increased. This article presents a genetic algorithm based on memory for dealing with dynamic optimization problems. The proposed algorithm focuses on explicit placement of memory schemes, and performs a comprehensive analysis on current design of Moving Peaks Benchmark (MPB) problem. The proposed algorithm used of the new strategy for update and retrieval memory. The MPB problem is the most proper benchmark for simulation of dynamic environments. The experimental study show the efficiency of the proposed approach for solving dynamic optimization problems in comparison with other algorithms presented in the literature.

dynamic optimization, Genetic algorithm, explicit memory, Offline Error