Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Adaptive evolutionary programming based on reinforcement learning
Information Sciences: an International Journal
Population distributions in biogeography-based optimization algorithms with elitism
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A probabilistic analysis of a simplified biogeography-based optimization algorithm
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Markov Models for Biogeography-Based Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary algorithm sandbox: a web-based graphical user interface for evolutionary algorithms
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An analysis of the equilibrium of migration models for biogeography-based optimization
Information Sciences: an International Journal
Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)
Computers and Operations Research
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
Blended biogeography-based optimization for constrained optimization
Engineering Applications of Artificial Intelligence
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
A dynamic system model of biogeography-based optimization
Applied Soft Computing
Biogeography migration algorithm for traveling salesman problem
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Handling multiple objectives with biogeography-based optimization
International Journal of Automation and Computing
An intuitive distance-based explanation of opposition-based sampling
Applied Soft Computing
Biogeography based optimization for multi-knapsack problems
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Multi-operator based biogeography based optimization with mutation for global numerical optimization
Computers & Mathematics with Applications
Variations of biogeography-based optimization and Markov analysis
Information Sciences: an International Journal
International Journal of Applied Evolutionary Computation
Research of Biogeography-Based Multi-Objective Evolutionary Algorithm
Journal of Information Technology Research
On the equivalences and differences of evolutionary algorithms
Engineering Applications of Artificial Intelligence
Emergency railway wagon scheduling by hybrid biogeography-based optimization
Computers and Operations Research
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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We propose a novel variation to biogeography-based optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO's migration rates to create oppositional BBO (OB BO). Additionally, a new opposition method named quasi-reflection is introduced. Quasi-reflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods. The oppositional algorithm is further revised by the addition of dynamic domain scaling and weighted reflection. Simulations have been performed to validate the performance of quasi-opposition as well as a mathematical analysis for a single-dimensional problem. Empirical results demonstrate that with the assistance of quasi-reflection, OB BO significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution.