Swarm intelligence
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Mathematics and Computers in Simulation
Particle Evolutionary Swarm Optimization Algorithm (PESO)
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
Self-adaptive velocity particle swarm optimization for solving constrained optimization problems
Journal of Global Optimization
Expert Systems with Applications: An International Journal
Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm
Computers & Mathematics with Applications
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
A New Vector Particle Swarm Optimization for Constrained Optimization Problems
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
Fast convergence strategy for particle swarm optimization using spread factor
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
An effective intelligent algorithm for stochastic optimization problem
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
An improved vector particle swarm optimization for constrained optimization problems
Information Sciences: an International Journal
Investigating the local-meta-model CMA-ES for large population sizes
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A mountain clustering based on improved PSO algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Swarm intelligence clustering algorithm based on attractor
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Information Sciences: an International Journal
On fast and accurate block-based motion estimation algorithms using particle swarm optimization
Information Sciences: an International Journal
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Particle swarm optimization (PSO) is a global metaheuristic that has been proved to be very powerful for optimizing a wide range of problems. However, PSO requires a large number of fitness evaluations to find acceptable (optimal or sub-optimal) solutions. If one single evaluation of the objective function is computationally expensive, the computational cost for the whole optimization run will become prohibitive. FESPSO, a new fitness estimation strategy, is proposed for particle swarm optimization to reduce the number of fitness evaluations, thereby reducing the computational cost. Different from most existing approaches which either construct an approximate model using data or utilize the idea of fitness inheritance, FESPSO estimates the fitness of a particle based on its positional relationship with other particles. More precisely, Once the fitness of a particle is known, either estimated or evaluated using the original objective function, the fitness of its closest neighboring particle will be estimated by the proposed estimation formula. If the fitness of its closest neighboring particle has not been evaluated using the original objective function, the minimum of all estimated fitness values on this position will be adopted. In case of more than one particle is located at the same position, the fitness of only one of them needs to be evaluated or estimated. The performance of the proposed algorithm is examined on eight benchmark problems, and the experimental results show that the proposed algorithm is easy to implement, effective and highly competitive.