Abstract
Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks.
Highlights
Swarm intelligence is known to be a family of approaches used for collective intelligence in problem solving
The following section introduces a major experimental study to demonstrate the performance of above-mentioned well-known bee algorithms and the revisions envisaged to enhance the capabilities via performances
The performance tests and analysis have been made using 6 numerical optimisation benchmarks, which are commonly used for the same purposes and given below
Summary
Swarm intelligence is known to be a family of approaches used for collective intelligence in problem solving. Swarm intelligence frameworks such as ant colony, particle swarm, artificial bee colonies algorithms impose use of population of solutions, here-forth called swarm of individuals. The main benefit of populationbased metaheuristic approaches, swarm intelligence algorithms, is that the algorithms nicely harmonise local search activities around various neighbourhoods without guaranteeing to cover the whole search space. Therein, the local search is devised, to a certain extend, for intensifying the search and enhancement activities are facilitated to diversify the search for managing change among neighbourhoods. Intensification is required to make the search
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