Abstract
A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well‐known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
Highlights
Global optimization GO is a field with applications in many areas of science, engineering, economics, and others, where mathematical modelling is used 1
In Collective Animal Behavior Algorithm (CAB), the searcher agents emulates a group of animals that interact with each other considering simple behavioral rules which are modeled as mathematical operators
The CAB algorithm presents two important characteristics: 1 CAB operators allow a better tradeoff between exploration and exploitation of the search space; 2 the use of its embedded memory incorporates information regarding previously found local minima potential solutions during the evolution process
Summary
Global optimization GO is a field with applications in many areas of science, engineering, economics, and others, where mathematical modelling is used 1. PSO consists of a swarm of particles which move towards best positions, seen so far, within a searchable space of possible solutions Another behavior-inspired approach is the ant colony optimization ACO algorithm proposed by Dorigo et al , which simulates the behavior of real ant colonies. A new optimization algorithm inspired by the collective animal behavior is proposed In this algorithm, the searcher agents emulate a group of animals that interact with each other based on simple behavioral rules which are modeled as mathematical operators. The searcher agents emulate a group of animals that interact with each other based on simple behavioral rules which are modeled as mathematical operators Such operations are applied to each agent considering that the complete group has a memory storing their own best positions seen so far, by using a competition principle.
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