Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
An introduction to differential evolution
New ideas in optimization
Journal of Global Optimization
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Ensemble of niching algorithms
Information Sciences: an International Journal
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Multi-modal Optimization refers to finding multiple global and local optima of a function in one single run, so that the user can have a better knowledge about different optimal solutions. Multiple global/local peaks generate extra difficulties for the optimization algorithms. Many niching techniques have been developed in literature to tackle multi-modal optimization problems. Clearing is one of the simplest and most effective methods in solving multi-modal optimization problems. In this work, an Ensemble of Clearing Differential Evolution (ECLDE) algorithm is proposed to handle multi-modal problems. In this algorithm, the population is evenly divided into 3 subpopulations and each of the subpopulations is assigned a set of niching parameters (clearing radius). The algorithms is tested on 12 benchmark multi-modal optimization problems and compared with the Clearing Differential Evolution (CLDE) with single clearing radius as well as a number of commonly used niching algorithms. As shown in the experimental results, the proposed algorithm is able to generate satisfactory performance over the benchmark functions.