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Genetic Algorithms and Machine Learning
Machine Learning
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Dynamic Clan Particle Swarm Optimization
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization
IEEE Transactions on Fuzzy Systems
Fractional particle swarm optimization in multidimensional search space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Multi-dimensional particle swarm optimization in dynamic environments
Expert Systems with Applications: An International Journal
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
Feedback learning particle swarm optimization
Applied Soft Computing
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
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Randomization in particle swarm optimization for global search ability
Expert Systems with Applications: An International Journal
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Early studies in particle swarm optimization (PSO) algorithm reveal that the social and cognitive components of swarm, i.e. memory swarm, tend to distribute around the problem's optima. Motivated by these findings, we propose a two-layer PSO with intelligent division of labor (TLPSO-IDL) that aims to improve the search capabilities of PSO through the evolution memory swarm. The evolution in TLPSO-IDL is performed sequentially on both the current swarm and the memory swarm. A new learning mechanism is proposed in the former to enhance the swarm's exploration capability, whilst an intelligent division of labor (IDL) module is developed in the latter to adaptively divide the swarm into the exploration and exploitation sections. The proposed TLPSO-IDOL algorithm is thoroughly compared with nine well-establish PSO variants on 16 unimodal and multimodal benchmark problems with or without rotation property. Simulation results indicate that the searching capabilities and the convergence speed of TLPSO-IDL are superior to the state-of-art PSO variants.