Abstract
This paper proposes a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the corporate predation of animals in nature. CPA utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target. Moreover, the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals’ selective abandonment behavior. This paper also presents a new way to deal with cross-border situations, whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm’s exploitation ability. The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm’s efficacy in optimizing engineering problems. The results show that the proposed algorithm exhibits competitive, superior performance in different search landscapes over the other algorithms. Moreover, the source code of the CPA will be publicly available after publication.
Highlights
Optimization methods are not limited to single-objective methods, and every single objective idea can be extended for dealing with more classes of problems that have more than one or many objective functions
This paper proposes a metaheuristic algorithm, named the Colony Predation Algorithm (CPA), inspired by social animals’ characteristics to solve the optimization problem
The algorithm achieves the correct balance between exploitation and exploration and can quickly converge in the early and middle stages without falling into Local Optimization (LO)
Summary
Optimization methods are not limited to single-objective methods, and every single objective idea can be extended for dealing with more classes of problems that have more than one or many objective functions. Metaheuristic algorithms[4] have attracted much attention and have been extensively used in numerous fields[6,7,8,9,10,11,12,13,14,15,16,17] Such popularity is attributed to the ability of MAs to solve many possible complex feature spaces in practical problems in neural network-based control[18,19], formation control[20], deep learning models and feature understanding[21,22], adaptive control[23,24], machine learning-based implements[25,26], and artificial intelligence[27].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
