Open Access Open Access  Restricted Access Subscription or Fee Access

Optimization algorithms for multi objective problems and its application to sustainable sources for supplementing energy crisis

Dr. A. K. Barisal

Abstract


Optimization, also called mathematical programming, is a collection of mathematical ideas and techniques used to solve quantitative issues in a variety of areas, including physics, biology, architecture, economics, and business. The subject evolved out of the recognition that quantitative issues in apparently disparate disciplines share significant mathematical features. As a result of this similarity, a wide variety of issues can be articulated and solved utilising the unified set of concepts and methods that comprise the subject of optimization. Numerous sectors such as electrical circuits, architecture, nutrition, economics, and transportation have benefited from these optimization algorithms. In contrast to classical computing, soft computing works with approximation models and provides solutions to complicated real-world situations

Keywords


Algorithms, Artificial Intelligence, Optimization, Machine Learning, Mutli modal and optimization.

Full Text:

PDF

References


Abido M A. Multiobjective optimal power flow using strength pareto evolutionary algorithm. In 39th International Universities Power Engineering Conference, 2004. UPEC 2004, volume 1, pages 457–461, September 2004.

Hosseini S H, Abbasy A, Tabatabaii I. A multiagent-based differential evolution algorithm for optimal reactive power dispatch in electricity markets. In International Conference on Power Engineering, Energy and Electrical Drives, 12-14 April 2007. POWERENG 2007, April 2007.

[ 3] El-Hawary M E, AlRashidi M R. Hybrid particle swarm optimization approach for solving the discrete opf problem considering the valve loading effects. IEEE Transactions on Power Systems, 22(4):2030–2038, 2007.

Venayagamoorthy G K, Aliyu U O, Bakare G A, Krost G. Differential evolution approach for reactive power optimization of nigerian grid system. In IEEE Power Engineering Society General Meeting 24-28 June, 2007, pages 1–6, 2007.

[ 5] Zoumas C E, Petridis V, Bakirtzis A G, Biskas P N. Optimal power flow by enhanced genetic algorithm. IEEE Transactions on Power Systems, 17(2):229–236, 2002.

[ 6] Tellidou A, Zoumas C E, Bakirtzis A G, Petridis V Tsakoumis, Biskas P N, Ziogos N P. Comparison of two metaheuristics with mathematical programming methods for the solution of opf. In Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems, 6-10 November 2005, page 6, 2005.

Cao Y J, Chao B, Guo C X. Improved particle swarm optimization algorithm for opf problems. In Proceedings of 2004 IEEE PES Power Systems Conference and Exposition, 1:233–238, 2004.

Liu C C, Chen Y L. Optimal multi-objective var planning using an interactive satisfying method. IEEE Transactions on Power Systems, 10(2):664–670, 1995.

Patvardhan C, Das D B. Useful multi-objective hybrid evolutionary approach to optimal power flow. In IEE Proceedings – Generation, Transmission and Distribution, 13 May 2003, volume 150, pages 275–282, May 2003.

Lambert-Torres G, Esmin A. Loss power minimization using particle swarm optimization. In International Joint Conference on Neural Networks, 2006. IJCNN ’06.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Journal of Microelectronics and Solid State Devices