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Ebrahimi, Mohammad Ali, Dehghan Dehnavi, Hassan, Mirabi, Mohammad, Honari, Mohammad Taghi, Sadeghian, Abolfazl. (1401). Solving NP hard problems using a new genetic algorithm. هنرهای کاربردی, 14(1), 275-285. doi: 10.22075/ijnaa.2022.26610.3364
Mohammad Ali Ebrahimi; Hassan Dehghan Dehnavi; Mohammad Mirabi; Mohammad Taghi Honari; Abolfazl Sadeghian. "Solving NP hard problems using a new genetic algorithm". هنرهای کاربردی, 14, 1, 1401, 275-285. doi: 10.22075/ijnaa.2022.26610.3364
Ebrahimi, Mohammad Ali, Dehghan Dehnavi, Hassan, Mirabi, Mohammad, Honari, Mohammad Taghi, Sadeghian, Abolfazl. (1401). 'Solving NP hard problems using a new genetic algorithm', هنرهای کاربردی, 14(1), pp. 275-285. doi: 10.22075/ijnaa.2022.26610.3364
Ebrahimi, Mohammad Ali, Dehghan Dehnavi, Hassan, Mirabi, Mohammad, Honari, Mohammad Taghi, Sadeghian, Abolfazl. Solving NP hard problems using a new genetic algorithm. هنرهای کاربردی, 1401; 14(1): 275-285. doi: 10.22075/ijnaa.2022.26610.3364

Solving NP hard problems using a new genetic algorithm

International Journal of Nonlinear Analysis and Applications
مقاله 23، دوره 14، شماره 1، فروردین 2023، صفحه 275-285    اصل مقاله (468.96 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.26610.3364
نویسندگان
Mohammad Ali Ebrahimi1؛ Hassan Dehghan Dehnavi* 1؛ Mohammad Mirabi2؛ Mohammad Taghi Honari1؛ Abolfazl Sadeghian1
1Department of Industrial Management, Yazd Branch, Islamic Azad University, Yazd, Iran
2Department of Industrial Engineering, Meybod University, Meybod, Iran
تاریخ دریافت: 17 دی 1400،  تاریخ بازنگری: 30 بهمن 1400،  تاریخ پذیرش: 27 اسفند 1400 
چکیده
Over the past few decades, a lot of meta-heuristics have been developed to solve N-P hard problems. Genetic algorithm, ant colony optimization, simulated annealing, electromagnetism algorithm and tabu search are some examples of meta-heuristics algorithms. These kinds of algorithms have two main classes: population-based and Trajectory. Many of these algorithms are inspired by various phenomena of nature. In this research, the author introduces a new population-based method inspired by the lifestyle of lions and the genetic algorithm’s structure called the new genetic algorithm (NGA). The social behaviour of lions and genetic operators like mutation and cross-over is the main structure of NGA. Finally, the NGA is compared with the hybrid genetic and hybrid ant colony optimization as the best existing algorithms in the literature. The experimental results have revealed that the NGA is competitive in terms of solution quality to solve the vehicle routing and scheduling problems as two main categories of N-P hard problems.
کلیدواژه‌ها
New genetic algorithm؛ N-P Hard problem؛ Scheduling, Vehicle routing problem, Genetic operator, Ant colony optimization
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