Advances in neural information processing systems 2
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Adaptive evolutionary programming based on reinforcement learning
Information Sciences: an International Journal
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
A team of continuous-action learning automata for noise-tolerant learning of half-spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
A new heuristic approach for non-convex optimization problems
Information Sciences: an International Journal
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Self-organizing potential field network: a new optimization algorithm
IEEE Transactions on Neural Networks
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm
Information Sciences: an International Journal
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Scale-free fully informed particle swarm optimization algorithm
Information Sciences: an International Journal
A Differential Covariance Matrix Adaptation Evolutionary Algorithm for real parameter optimization
Information Sciences: an International Journal
Machine learning for global optimization
Computational Optimization and Applications
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Genetic learning automata for function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper presents a new algorithm, Function Optimisation by Learning Automata (FOLA), to solve complex function optimisation problems. FOLA consists of multiple automata, in which each automaton undertakes dimensional search on a selected dimension of the solution domain. A search action is taken on a path which is identified in the search space by the path value, and the path value is updated using the values of the states visited in the past, via a state memory that enables better use of the information collected in the optimisation process. In this paper, FOLA is compared with two popularly used particle swarm optimisers and four newly-proposed optimisers, on nine complex multi-modal benchmark functions. The experimental results have shown that in comparison with the other optimisers, FOLA offers better performance for most of the benchmark functions, in terms of its convergence rate and accuracy, and it uses much less computation time to obtain accurate solutions, especially for high-dimensional functions. In order to explore the FOLA's potential for applications, it is also applied to solve an optimal power flow problem of power systems. FOLA is able to minimise the fuel cost and enhance the voltage stability of the power system more efficiently in comparison with the other algorithms.