Swarm intelligence
Journal of Global Optimization
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Advances in Differential Evolution
Advances in Differential Evolution
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Multi-population differential evolution with adaptive parameter control for global optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Co-evolutionary differential evolution with dynamic population size and adaptive migration strategy
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Enhance differential evolution with random walk
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Differential evolution enhanced with evolution path vector
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Differential evolution (DE) is an effective and efficient optimization algorithm that has been successfully applied to many problems. However, the DE performance significantly depends on the elaborate settings of its parameters. Designers of DE usually spend great efforts to find proper parameter settings because good parameter values usually vary with different problems. In order to enhance the efficiency and robustness of DE, this paper proposes a novel DE algorithm, PLADE, which uses the learning mechanism in particle swarm optimization (PSO), termed as PSO-Learning (PL) strategy, to adaptively control the DE parameters. PLADE encodes the DE parameters into each individual and evolve the parameters during the evolutionary process. The individuals that achieve good fitness and survive in the evolution imply good parameter settings, the poor individuals use the PL strategy to let their parameters learn from the parameters in the good individuals. With such a PL based parameter self-adaptation strategy, PLADE can evolve the parameters to better values and can adapt the parameters to match the requirements of different evolutionary states and different optimization problems. PLADE is tested by six benchmark functions with unimodal and multimodal characteristics. Experimental results show that PLADE not only outperforms conventional DE with fixed parameter settings, in terms of solution quality, convergence speed, and algorithm reliability, but also is better than or at least comparable to some other state-of-the-art adaptive DE variants.