Improved Jumping Particle Swarm Optimization Algorithm for Reservoir Operation
Subject Areas :
Article frome a thesis
rasol rajab pour
1
,
naser taleb bidokhti
2
,
gholamreza rakhshandehro
3
1 - گروه مهندسی عمران آب، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - استاد بخش مهندسی راه، ساختمان و محیط زیست، دانشکده مهندسی، دانشگاه شیراز، شیراز، ایران
3 - استاد بخش مهندسی راه، ساختمان و محیط زیست، دانشکده مهندسی، دانشگاه شیراز، شیراز، ایران
Received: 2015-08-16
Accepted : 2015-08-16
Published : 2015-07-23
Keywords:
Optimization,
PSO algorithm,
simple and hydropower operation,
Abstract :
Recently, metha-heuristic methods have been used as an efficient tools to solve complex engineering problems. One of these methods is JPSO algorithm, which, with a change in the nature of the jump of that algorithm in this research, it is possible to solve a graph-based problem with a new algorithm called G-JPSO. The simple and hydropower operation of dams is one of the important issues in the field of water resources management. One of the requirements to solve these problems in a discrete space is creating an appropriate graph. Application of this new developed algorithm on complex mathematical Ackley function and simple and hydropower operation of dams is reported in this paper. The results were compared with the ant colony algorithm. The results showed that the proposed algorithm reach the absolute optimal answer for the Ackley function, and it also showed that a minimum objective function for simple and hydropower operation with 200,000 iterations of the objective function are 1.07 and 7.83, respectively. The value of ant colony algorithm for these two applications are 0.93 and 10.1, respectively. This comparison demonstrates the ability of the developed algorithm in finding solutions close to the optimal solution with a reasonable computational cost.
References:
برهانی داریان، ع. و ا. مرادی، 1389. الگوریتم مورچگان پیوسته در بهینهسازی بهرهبرداری از سامانههای پیچیدهی چند آبگیره، مجله آب و فاضلاب، شماره 4، 81-91.
سامی کشکولی، ب و م. ج. منعم، 1388. توسعه و کاربرد شبیه بهینهسازی شبکههای آبیاری تحتفشار با استفاده از روش تلفیقی JPSO/LIDM، هشتمین کنفرانس هیدرولیک ایران، دانشگاه تهران، آذر 88.
مهدیپور، ا. و ا. بزرگ حداد، س. علیمحمدی،. 1393. بهرهوری بهینه از سامانه تلفیقی آبخوان-سد: رویکرد برنامهریزی ژنتیک، مجله مهندسی منابع آب، شماره 21، 51-66.
مهدیپور، ا. و ا. بزرگ حداد، 1391. بهینهسازی بهرهبرداری از آبگیرها سدهای چند منظوره با کاربرد الگوریتم بهینهسازی مجموعه ذرات، مجله آب و فاضلاب، شماره 4، 97-105.
Abbas Afshar, A., M. J., Emami, F., Masoumi, 2014, Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach., Engineering Optimization., 46(10), 170-181.
Afshar, M.H., 2012, Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms, Journal of Hydro-environment Research, 6, 75-87.
Al-kazemi, B. and C. K.Mohan, (2002), Multi-phase Discrete Particle Swarm Optimization. In: Fourth International Workshop on Frontiers in Evolutionary Algorithms, Kinsale, Ireland.
Becker L., and W., Yeh 1974, Optimization of real-time operation of a multiple reservoir system, water Resource .Res, 10(6), 1107-1112.
Bozorg Haddad, O., A., Afshar, and M.A., Marino, 2006, Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization, Water Resources Management, 20, 661-680.
Balter, A.M., and D.G. Fontane, (2006). “A multiobjective particle swarm optimization model for reservoir operations and planning.” Proceeding of International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, 1544-1552.
Cai X., Mckinney DC. and LS., Lasdon 2002, Piece-by-piece approach to solving large nonlinear water resources management models, ASCE J Water Resour Plann Mgmt, 127(6),363-368.
Clerc, M. (2000), Discrete particle swarm optimization illustrated by the traveling salesman problem, http://www.mauriceclerc.net.
Coello, C.A., and M.S. Lechunga, (2002). “MOPSO: A proposal for multiple objective particle swarm optimization.” Proceeding of IEEE Congress on Evolutionary omputation, IEEE Service Center. Piscataway, NJ, 2, 1677-1681.
East V., M.J. Hall,1994, water resource system optimization using genetic algorithms, hydro informatics’94, Pro., 1st Int. Conf. on Hydro informatics, Balkerma, Rotterdam, The Netherlands, 225-231.
Gen, M., and R. W., Cheng, (1997). Genetic Algorithm and Engineering Design. John Wiley and Sons, Inc.
Hendtlass, T. (2003), Preserving Diversity in Particle Swarm Optimization, in: Lecture Notes in Computer Science, vol. 2718, Springer, pp: 4104-4108.
Jalali, M. R. A.Afshar, and M.A., Mariño, 2007, Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem, Water Resources Management, 21(9), 1429-1447.
Kennedy, J. and R. Eberhart, (1997), Adiscrete binary version of the particle swarm algorithm. In: IEEE Conference on Systems, Man, and Cybernerics, vol 5, pp 4104-4108.
Kumar, D., and J. Reddy, (2007). “Multiple reservoir operation using particle swarm optimization.” J. of Water Resources Planning and Management, 133(3), 192-202.
Marino, M.A. and H.A. Loaiciga, Dynamic model for multi reservoir operation", Water Resource Res., 21(5), pp. 619-630 (1985).
Meraji, S.H., M.H., Afshar, and A. Afshar, (2006). “Reservoir operation by particle swarm optimization algorithm.” 7th International Conference of Civil Engineering, Tehran, Iran.
Moeini, R. and M. H. Afshar. (2009), Application of an Ant Colony Optimization Algorithm for Optimal Operation of Reservoirs: A Comparative Study of Three Proposed Formulations. Civil engineering. Vol. 16, No. 4, pp. 273-285.
Moreno-Perez, J. A., J. P, Castro-Gutierrez, F. J., Martinez-Garcia B., Melian, J. M. Moreno-Vega, and J. Ramos, (2007), Discrete Particle Swarm Optimization for the p-median problem. In: Procceedings of the 7th Metaheuristics International Conference, Montreal, Canada.
Peng, C.H, Buras N., 2000, Dynamic operation of a surface water resources systems, Water Resour Res, 36(9),2701-2709.
Pugh, J., and A. Martinoli, (2006), Discrete multi-valued particle swarm optimization. In: Proceedings of IEEE Swarm Intelligence Symposium, vol 1, pp 103-110.
Shi, X. H., Y. C., Liang, H. P., Lee, C. Lu, and Wang, Q. X. (2007), Particle swarm optimization-based algorithms for TSP and generalized TSP, Information Processing Letters, 103 (2007) 169-176.
Wang, K-P., L., Huang, C. Zhou, and W. Pang, (2003), Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi’an, 2-5 November 2003.
Wardlaw R., M., Sharif 1999, Evaluation of genetic algorithms For Optimal Reservoir system operation, Journal of water resources planning and management, January 1999, 25-33.
Yang, S. M. Wang, and L. Jiao, (2004), A Quantum Particle Swarm Optimization. In: Proceedings of CEC2004, the Congress on Evolutionary Computing, vol 1, pp 320-324.
Yeh WW-G., 1985, Reservoir management and operations models: A state-of-the-art review. , Water Resource Reservoir, 1797-818.
Zhang R, J, and Y., Zhou Wang 2012, Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects, International Journal of Electrical Power & Energy Systems, 42, 384-95.
_||_