2016/09/26 by Омкар Кулкарни, Kulkarni, Omkar, Ninad Kulkarni +8
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Manufacturing Process and Optimization #Metaheuristic Optimization Algorithms Research #Scheduling and Optimization Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.1610.06009
arxiv created 2016/09/26 · openalex publication_date 2016/09/26 · arxiv updated 2016/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.