PMID- 37163175
OWN - NLM
STAT- MEDLINE
DCOM- 20230512
LR  - 20230512
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2023
DP  - 2023
TI  - A Reference Point-Based Evolutionary Algorithm Solves Multi and Many-Objective 
      Optimization Problems: Method and Validation.
PG  - 4387053
LID - 10.1155/2023/4387053 [doi]
LID - 4387053
AB  - The integration of a decision maker's preferences in evolutionary multi-objective 
      optimization (EMO) has been a common research scope over the last decade. In the 
      published literature, several preference-based evolutionary approaches have been 
      proposed. The reference point-based non-dominated sorting genetic (R-NSGA-II) 
      algorithm represents one of the well-known preference-based evolutionary 
      approaches. This method mainly aims to find a set of the Pareto-optimal solutions 
      in the region of interest (ROI) rather than obtaining the entire Pareto-optimal 
      set. This approach uses Euclidean distance as a metric to calculate the distance 
      between each candidate solution and the reference point. However, this metric may 
      not produce desired solutions because the final minimal Euclidean distance value 
      is unknown. Thus, determining whether the true Pareto-optimal solution is 
      achieved at the end of optimization run becomes difficult. In this study, 
      R-NSGA-II method is modified using the recently proposed simplified 
      Karush-Kuhn-Tucker proximity measure (S-KKTPM) metric instead of the Euclidean 
      distance metric, where S-KKTPM-based distance measure can predict the convergence 
      behavior of a point from the Pareto-optimal front without prior knowledge of the 
      optimum solution. Experimental results show that the algorithm proposed herein is 
      highly competitive compared with several state-of-the-art preference-based EMO 
      methods. Extensive experiments were conducted with 2 to 10 objectives on various 
      standard problems. Results show the effectiveness of our algorithm in obtaining 
      the preferred solutions in the ROI and its ability to control the size of each 
      preferred region separately at the same time.
CI  - Copyright (c) 2023 Mohammed Jameel and Mohamed Abouhawwash.
FAU - Jameel, Mohammed
AU  - Jameel M
AUID- ORCID: 0000-0001-5459-341X
AD  - Department of Mathematics, Sana'a University, Sana'a 13509, Yemen.
FAU - Abouhawwash, Mohamed
AU  - Abouhawwash M
AUID- ORCID: 0000-0003-2846-4707
AD  - Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 
      35516, Egypt.
AD  - Department of Computational Mathematics Science and Engineering (CMSE), Michigan 
      State University, East Lansing, MI 48824, USA.
LA  - eng
PT  - Journal Article
DEP - 20230114
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - *Algorithms
MH  - *Biological Evolution
PMC - PMC10164242
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/05/10 12:42
MHDA- 2023/05/12 07:06
PMCR- 2023/01/14
CRDT- 2023/05/10 11:25
PHST- 2022/08/08 00:00 [received]
PHST- 2022/11/30 00:00 [revised]
PHST- 2022/12/02 00:00 [accepted]
PHST- 2023/05/12 07:06 [medline]
PHST- 2023/05/10 12:42 [pubmed]
PHST- 2023/05/10 11:25 [entrez]
PHST- 2023/01/14 00:00 [pmc-release]
AID - 10.1155/2023/4387053 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2023 Jan 14;2023:4387053. doi: 10.1155/2023/4387053. 
      eCollection 2023.