Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Enhancing particle swarm optimization using generalized opposition-based learning
Information Sciences: an International Journal
A diversity-guided quantum-behaved particle swarm optimization algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
International Journal of Bio-Inspired Computation
An improved particle swarm optimisation for solving generalised travelling salesman problem
International Journal of Computing Science and Mathematics
Particle swarm optimisation: time for uniformisation
International Journal of Computing Science and Mathematics
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Particle swarm optimisation PSO is a global optimisation technique, which has shown a good performance on many problems. However, PSO easily falls into local minima because of quick losing of diversity. Some diversity-guided PSO algorithms have been proposed to maintain diversity, but they often slow down the convergence rate. In this paper, we propose an improved diversity-guided PSO algorithm, namely IDPSO, which employs a local search to enhance the exploitation. In addition, a concept of generalised opposition-based learning GOBL is utilised for population initialisation and generation jumping to find high quality of candidate solutions. Experiments are conducted on a set of benchmark functions. Results show that our approach obtains a promising performance when compared with other PSO variants.