EMCSO: AN ELITIST MULTI-OBJECTIVE CAT SWARM OPTIMIZATION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

OROUSKHANI MEYSAM; TESHNEHLAB MOHAMMAD; NEKOUI MOHAMMAD ALI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING) Year:2018 | Volume:11 | Issue:2 (24) Page(s): 107-117

Download Full-Text

View:

300

Download:

138

Cites:

Information Journal Paper

Title

EMCSO: AN ELITIST MULTI-OBJECTIVE CAT SWARM OPTIMIZATION

Author(s)

OROUSKHANI MEYSAM | TESHNEHLAB MOHAMMAD | NEKOUI MOHAMMAD ALI | Issue Writer Certificate

Keywords

MULTI-OBJECTIVE CAT SWARM OPTIMIZATION

NON-DOMINATED SORTING

CROWDING DISTANCE

OPPOSITION-BASED LEARNING

Abstract

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimization algorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses the nondominated sorting method to find the solutions as close as to POF and CROWDING DISTANCE technique to obtain a uniform distribution among the non-dominated solutions. Also, the algorithm is allowed to keep the elites of population in reproduction process and use an opposition based learning method for population initialization to enhance the convergence speed. The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based on performance measures of generational distance (GD), inverted GD, spread, and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

OROUSKHANI, MEYSAM, TESHNEHLAB, MOHAMMAD, & NEKOUI, MOHAMMAD ALI. (2018). EMCSO: AN ELITIST MULTI-OBJECTIVE CAT SWARM OPTIMIZATION. JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING), 11(2 (24)), 107-117. SID. https://sid.ir/paper/709019/en

Vancouver: Copy

OROUSKHANI MEYSAM, TESHNEHLAB MOHAMMAD, NEKOUI MOHAMMAD ALI. EMCSO: AN ELITIST MULTI-OBJECTIVE CAT SWARM OPTIMIZATION. JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING)[Internet]. 2018;11(2 (24)):107-117. Available from: https://sid.ir/paper/709019/en

IEEE: Copy

MEYSAM OROUSKHANI, MOHAMMAD TESHNEHLAB, and MOHAMMAD ALI NEKOUI, “EMCSO: AN ELITIST MULTI-OBJECTIVE CAT SWARM OPTIMIZATION,” JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING), vol. 11, no. 2 (24), pp. 107–117, 2018, [Online]. Available: https://sid.ir/paper/709019/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops