Journal of Global Optimization
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
International Journal of Bio-Inspired Computation
Bio-inspired methods for fast and robust arrangement of thermoelectric modulus
International Journal of Bio-Inspired Computation
Using APPM-trained ANN to solve stochastic expected value mode
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
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The major application of stochastic intelligent methods in optimisation, control and management of complex systems is transparent. Many researchers try to develop collective intelligent techniques and hybrid meta-heuristic models for improving the reliability of such optimisation algorithms. In this paper, a new optimisation method that is the simulation of 'the great salmon run' (TGSR) is developed. This simulation provides a powerful tool for optimising complex multi-dimensional and multi-modal problems. For demonstrating the high robustness and acceptable quality of TGSR, it is compared with most of the well-known proposed optimisation techniques such as parallel migrating genetic algorithm (PMGA), simulate annealing (SA), differential evolutionary algorithm (DEA), particle swarm optimisation (PSO), bee algorithm (BA), artificial bee colony (ABC), firefly algorithm (FA) and cuckoo search (CS). The obtained results confirm the predominance of the proposed method in both robustness and quality in different optimisation problems.