{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:25:21Z","timestamp":1741667121285,"version":"3.38.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,9,16]]},"abstract":"<jats:p>In recent years, the Cuckoo Optimization Algorithm (COA) has been widely used to solve various optimization problems due to its simplicity, efficacy, and capability to avoid getting trapped in local optima. However, COA has some limitations such as low convergence when it comes to solving constrained optimization problems with many constraints. This study proposes a new modified and adapted version of the Cuckoo optimization algorithm, referred to as MCOA, that overcomes the challenge of solving constrained optimization problems. The proposed adapted version introduces a new coefficient that reduces the egg-laying radius, thereby enabling faster convergence to the optimal solution. Unlike previous methods, the new coefficient does not require any adjustment during the iterative process, as the radius automatically decreases along the iterations. To handle constraints, we employ the Penalty Method, which allows us to incorporate constraints into the optimization problem without altering its formulation. To evaluate the performance of the proposed MCOA, we conduct experiments on five well-known case studies. Experimental results demonstrate that MCOA outperforms COA and other state-of-the-art optimization algorithms in terms of both efficiency and robustness. Furthermore, MCOA can reliably find the global optimal solution for all the tested problems within a reasonable iteration number.<\/jats:p>","DOI":"10.3233\/idt-240306","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T17:47:53Z","timestamp":1719596873000},"page":"2307-2337","source":"Crossref","is-referenced-by-count":0,"title":["A new accurate and fast convergence cuckoo search algorithm for solving constrained engineering optimization problems"],"prefix":"10.1177","volume":"18","author":[{"given":"Mahdi","family":"Abdollahi","sequence":"first","affiliation":[{"name":"School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand"}]},{"given":"Asgarali","family":"Bouyer","sequence":"additional","affiliation":[{"name":"Faculty of Computer Engineering and Information Technology, Azarbaijan Shahid Madani University, Tabriz, Iran"},{"name":"Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey"}]},{"given":"Bahman","family":"Arasteh","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey"},{"name":"Department of Computer Science, Khazar University, Baku, Azerbaijan"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDT-240306_ref1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.compchemeng.2009.09.006","article-title":"A hybrid genetic algorithm for twice continuously differentiable NLP problems","volume":"34","author":"Yuan","year":"2010","journal-title":"Computers & Chemical Engineering"},{"key":"10.3233\/IDT-240306_ref2","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1007\/s11227-016-1660-8","article-title":"Improved cuckoo optimization algorithm for solving systems of nonlinear equations","volume":"72","author":"Abdollahi","year":"2016","journal-title":"The Journal of Supercomputing"},{"issue":"4","key":"10.3233\/IDT-240306_ref4","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1080\/03052159308940980","article-title":"Mixed-discrete nonlinear optimization with simulated annealing","volume":"21","author":"Zhang","year":"1993","journal-title":"Engineering Optimization"},{"issue":"2","key":"10.3233\/IDT-240306_ref6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0166-3615(99)00046-9","article-title":"Use of a self-adaptive penalty approach for engineering optimization problems","volume":"41","author":"Coello","year":"2000","journal-title":"Computers in Industry"},{"issue":"4","key":"10.3233\/IDT-240306_ref7","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TEVC.2003.814902","article-title":"Society and civilization: An optimization algorithm based on the simulation of social behavior","volume":"7","author":"Ray","year":"2003","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"6","key":"10.3233\/IDT-240306_ref9","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1109\/TSMCB.2006.873185","article-title":"Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems","volume":"36","author":"Krohling","year":"2006","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"10.3233\/IDT-240306_ref10","unstructured":"Cagnina LC, Esquivel SC, Coello CAC. 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