Chase,Pounce,and Escape Optimization Algorithm  

作　　者：Adel Sabry Eesa 

机构地区：[1]Computer Science Department,Faculty of Science,University of Zakho,Duhok City,42001,Iraq

出　　处：《Intelligent Automation & Soft Computing》2024年第4期697-723,共27页智能自动化与软计算（英文）

摘　　要：While many metaheuristic optimization algorithms strive to address optimization challenges,they often grapple with the delicate balance between exploration and exploitation,leading to issues such as premature convergence,sensitivity to parameter settings,and difficulty in maintaining population diversity.In response to these challenges,this study introduces the Chase,Pounce,and Escape(CPE)algorithm,drawing inspiration from predator-prey dynamics.Unlike traditional optimization approaches,the CPE algorithm divides the population into two groups,each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima.By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution,the CPE algorithm demonstrates superior convergence properties.Additionally,the inclusion of a pouncing process facilitates rapid movement towards optimal solutions.Through comprehensive evaluations across various optimization scenarios,including standard test functions,Congress on Evolutionary Computation(CEC)-2017 benchmarks,and real-world engineering challenges,the effectiveness of the CPE algorithm is demonstrated.Results consistently highlight the algorithm’s performance,surpassing that of other well-known optimization techniques,and achieving remarkable outcomes in terms of mean,best,and standard deviation values across different problem domains,underscoring its robustness and versatility.

关 键 词：Bio-inspired optimization METAHEURISTIC chase pounce and escape optimizer collective behavior engineering design problems 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]