Problem difficulty analysis for particle swarm optimization: deception and modality
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Computers and Operations Research
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
Multi-start JADE with knowledge transfer for numerical optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Memetic algorithms for continuous optimisation based on local search chains
Evolutionary Computation
A critical assessment of some variants of particle swarm optimization
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A rank based particle swarm optimization algorithm with dynamic adaptation
Journal of Computational and Applied Mathematics
A new evolutionary search strategy for global optimization of high-dimensional problems
Information Sciences: an International Journal
Repair methods for box constraints revisited
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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We use evolutionary computation (EC) to automatically find problems which demonstrate the strength and weaknesses of modern search heuristics. In particular, we analyze particle swarm optimization (PSO), differential evolution (DE), and covariance matrix adaptation-evolution strategy (CMA-ES). Each evolutionary algorithm is contrasted with the others and with a robust nonstochastic gradient follower (i.e., a hill climber) based on Newton-Raphson. The evolved benchmark problems yield insights into the operation of PSOs, illustrate benefits and drawbacks of different population sizes, velocity limits, and constriction (friction) coefficients. The fitness landscapes made by genetic programming reveal new swarm phenomena, such as deception, thereby explaining how they work and allowing us to devise better extended particle swarm systems. The method could be applied to any type of optimizer.