Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed

Multi-Objective Optimization of Periodical Objectives within a Process

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)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: 2602-3199 2021, 16, 1068585
DOI: 10.55549/epstem.1068585

Abstract

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems 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 duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based 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 far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

Keywords

optimization; multi-objective optimization; decision making; Time

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

Comments (1)

Comment 1
Received: 3 February 2022
Commenter: Moses Oyaro Okello
Commenter's Conflict of Interests: Author
Comment: Title and abstract modified

Sample examples  presented 

Litterature review expanded too.
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.