Abstract
In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global) are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB). 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 location, 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, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.
Highlights
A large number of real-world problems can be considered as multimodal function optimization subjects
This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB)
The main individual, that is considered as pivot in the equations, is not the best but one element of a set which is contained in memories that store the best individual seen so far
Summary
A large number of real-world problems can be considered as multimodal function optimization subjects. They have demonstrated to deliver better results than those based on the HT considering accuracy, speed, and robustness [41] Such approaches have produced several robust circle detectors using different optimization algorithms such as genetic algorithms (GAs) [41], harmony search (HSA) [42], electromagnetism-like (EMO) [43], differential evolution (DE) [44], and bacterial foraging optimization (BFOA) [45]. A new multimodal optimization algorithm based on the collective animal behavior is proposed and applied to multicircle detection. This paper proposes a new optimization algorithm inspired by the collective animal behavior 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 evolutionary operators.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
