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 the subset of optimization which deals with minimization of several objective functions more than the conventional one objective optimization. These have useful application in decision making. Many of the current methodologies addresses challenges and solutions to multi-objective optimization problem which attempt to simultaneously solve several objectives with multiple constraints subjoined to each objective. Such as evolutionary algorithm, genetic algorithm, flower pollination algorithm, and many more. However, most challenges in MOO are generally subjected to linear inequality constraints that prevent all objectives from being optimized at once. This paper discusses some approaches presented by scholars in MOO 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 life time unlike permanent objectives which are optimized once for the entire process life time. A methodology based on partial optimization which optimizes each objective iteratively and weight convergence method which optimizes sub-group of objectives is given. Furthermore, another methods is introduced which involve objective classification ranking, estimation and prediction where objectives are classified base on their properties, and rank using a given criteria and in addition estimated for its optimal weight point if it certifies a coveted optimal weight point. Then finally predicted to find how much it deviates from the estimated point.
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.
