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
Swarm intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
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
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Swarms in dynamic environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimization using particle swarms with near neighbor interactions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
Evolutionary multimodal optimization using the principle of locality
Information Sciences: an International Journal
Credit portfolio management using two-level particle swarm optimization
Information Sciences: an International Journal
Cooperative Velocity Updating model based Particle Swarm Optimization
Applied Intelligence
Hi-index | 0.07 |
Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based force-imitated PSO (NN-FPSO) algorithm, each particle explores the promising regions where it resides under the composite forces produced by the ''near-neighbor attractor'' and ''near-neighbor repeller'', which are selected from the set of memorized personal best positions and the current swarm based on the principles of ''superior-and-nearer'' and ''inferior-and-nearer'', respectively. These two forces pull and push a particle to search for the nearby optimum. Hence, particles can simultaneously locate multiple optima quickly and precisely. Experiments are carried out to investigate the performance of NN-FPSO in comparison with a number of state-of-the-art PSO algorithms for locating multiple optima over a series of multimodal benchmark test functions. The experimental results indicate that the proposed NN-FPSO algorithm can efficiently locate multiple optima in multimodal fitness landscapes.