How to solve it: modern heuristics
How to solve it: modern heuristics
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Applying Self-Organised Criticality to Evolutionary Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Center particle swarm optimization
Neurocomputing
Mean particle swarm optimisation for function optimisation
International Journal of Computational Intelligence Studies
Journal of Global Optimization
Simplifying Particle Swarm Optimization
Applied Soft Computing
Boid particle swarm optimisation
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A soft computing system for day-ahead electricity price forecasting
Applied Soft Computing
A metamorphosis algorithm for the optimization of a multi-node OLAP system
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Hybridisation of particle swarm optimisation with area concentrated search
International Journal of Knowledge-based and Intelligent Engineering Systems
Heterogeneous particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
A new memetic algorithm using particle swarm optimization and genetic algorithm
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A new PSO model mimicking bio-parasitic behavior
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A hybrid particle swarm optimization algorithm based on nonlinear simplex method and tabu search
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Psychological model of particle swarm optimization based multiple emotions
Applied Intelligence
Information Sciences: an International Journal
Biological plausibility in optimisation: an ecosystemic view
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Taguchi-Particle Swarm Optimization for Numerical Optimization
International Journal of Swarm Intelligence Research
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Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the LifeCycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to create a generally well-performing search heuristics. In the LifeCycle model, we consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. First results from a comparison of our new approach with the single search algorithms indicate a generally good performance in numerical optimization.