Abstract
Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.
Highlights
IntroductionSwarm Intelligence (SI) is the direct result of self-organization in which the interactions of lower-level components create a global-level dynamic structure that may be regarded as intelligence [2]
Swarm Intelligence (SI) is defined as the collective problem-solving capabilities of social animals [1].SI is the direct result of self-organization in which the interactions of lower-level components create a global-level dynamic structure that may be regarded as intelligence [2]
In this sub-section, the ANSSA-based Bees-inspired Algorithm (BA) was tested on benchmark functions and the results were compared with those obtained using the basic BA and other well-known optimization techniques such as Evolutionary Algorithm (EA), Particle Swarm Optimization (PSO), Artificial Bee
Summary
SI is the direct result of self-organization in which the interactions of lower-level components create a global-level dynamic structure that may be regarded as intelligence [2]. These lower level interactions are guided by a simple set of rules that individuals of the colony follow without any knowledge of its global effects [2]. A hierarchical structure is used only for dividing up the necessary duties; there is no control over individuals but over instincts This creates dynamic and efficient structures that help the colony to survive despite many challenges [2]
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