Version 1
: Received: 19 May 2020 / Approved: 20 May 2020 / Online: 20 May 2020 (11:24:26 CEST)
Version 2
: Received: 23 October 2020 / Approved: 27 October 2020 / Online: 27 October 2020 (11:42:31 CET)
Version 3
: Received: 1 February 2022 / Approved: 3 February 2022 / Online: 3 February 2022 (17:30:32 CET)
Okello, M. O. (2021). Time Governed Multi-Objective Optimization . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 16 , 167-181 . DOI: 10.55549/epstem.1068585
Okello, M. O. (2021). Time Governed Multi-Objective Optimization . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 16 , 167-181 . DOI: 10.55549/epstem.1068585
Okello, M. O. (2021). Time Governed Multi-Objective Optimization . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 16 , 167-181 . DOI: 10.55549/epstem.1068585
Okello, M. O. (2021). Time Governed Multi-Objective Optimization . The Eurasia Proceedings of Science Technology Engineering and Mathematics , 16 , 167-181 . DOI: 10.55549/epstem.1068585
Abstract
Multi-objective optimization (MOO) is an optimization involving minimization of several objective functions more than the conventional one objective optimization which have useful applications in Engineering. Many of the current methodologies addresses challenges and solutions to multi-objective optimization problem, which attempts to solve simultaneously several objectives with multiple constraints, subjoined to each objective. Most challenges in MOO are generally subjected to linear inequality constraints that prevent all objectives from being optimized simultaneously. This paper takes short survey and deep analysis of Random and Uniform Entry-Exit time of objectives. It then breaks down process into sub-process and then presents some new concepts by introducing methods in solving problem in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime unlike permanent objectives, which are optimized once for the entire process lifetime. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives is given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified base on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how much it deviates from the estimated optimal weight point. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis presented, the concept is worthy of igniting further research and application.
Keywords
optimization; multi-objective optimization; decision making; Time
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Moses Okello
Commenter's Conflict of Interests: Author