Abstract
Traditional list-based threshold-accepting LBTA algorithm is similar with simulated annealing SA algorithm, depends on an intense local search method, and utilises a list filling procedure with threshold values to search the solution space effectively. Inspired by the learning ability of particle swarm optimisation PSO, multi-agent LBTA MLBTA involves the learning knowledge to guide its sampling, explores the solution space in a co-evolution mode. Compare with multi-agent SA MSA algorithm adapting the same local search version, MLBTA incorporates a dynamic list of threshold values which is adapted according to the topology of the solution space and tunes only one parameter. Dispense with sophisticated parameters as MSA, MLBTA balances the intensification and diversification iteratively. Computational results on functions optimisation and protein structure prediction PSP problems show that MLBTA algorithm achieves better or comparable performances with MSA.
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