Abstract
Most heuristics for discrete optimization problems consist of two phases: a greedy-based construction phase followed by an improvement (local search) phase. Although the best solutions are usually generated after the improvement phase, there is usually a high computational cost for employing a local search algorithm. This paper seeks another alternative to reduce the computational burden of a local search while keeping solution quality by embedding intelligence in metaheuristics. A modified version of Path Relinking is introduced to replace the local search in the improvement phase of Meta-RaPS (Meta-Heuristic for Randomized Priority Search) which is currently classified as a memoryless metaheuristic. The new algorithm is tested using the 0–1 multidimensional knapsack problem, and it is observed that it could solve even the largest benchmark problems in significantly less time while maintaining solution quality compared to other algorithms in the literature.
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.
