Solving large scale optimization problems by opposition-based differential evolution (ODE)
WSEAS Transactions on Computers
Investigating in scalability of opposition-based differential evolution
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
CODEQ: an effective metaheuristic for continuous global optimisation
International Journal of Metaheuristics
Opposition-based artificial bee colony algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Swarm algorithms with chaotic jumps applied to noisy optimization problems
Information Sciences: an International Journal
Enhancing particle swarm optimization using generalized opposition-based learning
Information Sciences: an International Journal
An intuitive distance-based explanation of opposition-based sampling
Applied Soft Computing
A hybrid algorithm for artificial neural network training
Engineering Applications of Artificial Intelligence
Hardware opposition-based PSO applied to mobile robot controllers
Engineering Applications of Artificial Intelligence
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Particle Swarm Optimization (PSO) is a simple, reliable, and efficient optimization algorithm. However, it suffers from a weakness, losing the efficiency over optimization of noisy problems. In many real-word optimization problems we are faced with noisy environments. This paper presents a new algorithm to improve the efficiency of PSO to cope with noisy optimization problems. It employs opposition-based learning for swarm initialization, generation jumping, and also improving swarm's best member. A set of commonly used benchmark functions is employed for experimental verification, and the results show clearly the new algorithm outperforms PSO in terms of convergence speed and global search ability.