Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems.
Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions.
The final refinement of the simplified model parameters may be accomplished by a multimodal optimization procedure.
This paper introduces a new hybrid algorithm for locating all solutions in multimodal optimization problems.
Two experiments are conducted on stochastic multimodal optimization problem and stochastic resource allocation problem.
The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions.
Performance of the proposed algorithm is compared with a number of state-of-the-art multimodal optimization algorithms over a benchmark-suite comprising of 21 basic multimodal problems and 7 composite multimodal problems.
It is natural to extend conventional unimodal optimization to challenging multimodal optimization design of composite structures by means of emergent niching particle swarm optimization (PSO), due to multimodal characteristics of composite structures by nature.
The results show that the proposed approach achieves competitive performance compared with several state-of-the-art multimodal optimization algorithms.
To explore the principle in evolutionary computation, crowding differential evolution is incorporated with locality for multimodal optimization.
