Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
New ideas in optimization
The ant colony optimization meta-heuristic
New ideas in optimization
An introduction to differential evolution
New ideas in optimization
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th 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
Comparison between Genetic Algorithms and 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
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Adaptive Heuristic Applied to Large Constraint Optimisation Problem
Large-Scale Scientific Computing
CODEQ: an effective metaheuristic for continuous global optimisation
International Journal of Metaheuristics
Adaptive intelligence applied to numerical optimisation
NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
Information Sciences: an International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration
Journal of Network and Computer Applications
Free Pattern Search for global optimization
Applied Soft Computing
An improved free search approach for energy optimization in wireless sensor networks
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
Hi-index | 0.07 |
The article presents a novel population-based optimisation method, called Free Search (FS). Essential peculiarities of the new method are introduced. The aim of the study is to identify how robust is Free Search. Explored and compared are four different population-based optimisation methods, namely Genetic Algorithm (in real coded BLX-@a modification), Particle Swarm Optimisation, Differential Evolution and Free Search. They are applied to five non-linear, heterogeneous, numerical, optimisation problems. The achieved results suggest that Free Search has stable robust behaviour on explored tests; FS can cope with heterogeneous optimisation problems; FS is applicable to unknown (black-box) real-world optimisation tasks.