Abstract
Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.
Highlights
Collective intelligence is one of the approaches commonly found useful for problem-solving in the modern times
The marginal achievement is plotted excluding the challenging benchmark problems, where bees algorithm (BA) does not improve, but both hybrid algorithm (Hybrid) and artificial bee colony (ABC) improve with exclusion of challenging benchmarks as well as with the growing number of iterations
The Hybrid algorithm is tested with another numerical optimisation case, which is used for optimising the weights of feed-forward neural network models used in classification problems
Summary
Collective intelligence is one of the approaches commonly found useful for problem-solving in the modern times. Swarm intelligence algorithms do require intensification of the search in local regions as they deliver very diverse search by default This feature applies to the algorithms developed inspired of the collective behaviour of honeybees, where a number of bees algorithm (BA) [25] and artificial bee colony (ABC) [12] variants have been redesigned to manage/handle such a harmony among various search actions. The main aim of this paper is to propose a hybridising framework to merge the strong search capabilities of both BA and ABC to seek for higher efficiency in problemsolving Both algorithms, BA and ABC, have been reviewed first to identify the strengths and weaknesses with respect to intensification in search. A hybridisation approach is introduced to merge the strengths of both into a new algorithm for further intensification and improved diversification In this respect, the contributions of this paper can be listed as follows:.
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