A Cumulative Multi-Niching Genetic Algorithm for Multimodal Function Optimization
This addresses optimization problems with computationally-expensive multimodal functions, offering significant efficiency gains for researchers and practitioners in fields like engineering design, but it is incremental as it builds on existing multi-niching genetic algorithms.
The paper tackled multimodal function optimization by proposing a cumulative multi-niching genetic algorithm (CMN GA) that retains all individuals to use information from every evaluation, resulting in an order-of-magnitude reduction in objective function evaluations and improved convergence ability compared to other algorithms.
This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control over the design space reduces unnecessary objective function evaluations. The algorithm's novel arrangement of genetic operations provides fast and robust convergence to multiple local optima. Benchmark tests alongside three other multi-niching algorithms show that the CMN GA has a greater convergence ability and provides an order-of-magnitude reduction in the number of objective function evaluations required to achieve a given level of convergence.