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Ground Motion Record selection Using Multi-objective Optimization algorithms: A Comparative Study

PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2019年第3期63卷 812-822页

作者： Kaveh, Ali Moghanni, Roya Mahdipour Javadi, Seyed Mohammad Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran *** Iran

Performing time history dynamic analysis using site-specific ground motion records according to the increasing interest in the performance-based earthquake engineering has encouraged studies related to site-specific Ground Motion Record (GMR) selection methods. This study addresses a ground motion record selection approach based on three different multi-objective optimization algorithms including Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic algorithm II (NSGA-II) and pareto envelope-based selection algorithm II (PESA-II). The method proposed in this paper selects records efficiently by matching dispersion and mean spectrum of the selected record set and target spectrums in a predefined period. Comparison between the results shows that NSGA II performs better than the other algorithms in the case of GMR selection.

关键词： ground motion record selection multi-objective particle swarm optimization non-dominated sorting genetic algorithm II pareto envelope-based selection algorithm

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A comparative analysis of MOPSO, NSGA-II, SPEA2 and PESA2 for multi-objective optimal power flow 2

A comparative analysis of MOPSO, NSGA-II, SPEA2 and PESA2 fo...

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2nd International Conference on Energy, Power and Environment - Towards Smart Technology (ICEPE)

作者： Jain, Abhishek Lalwani, Soniya Lalwani, Mahendra Rajasthan Tech Univ Dept Elect Engn Kota Rajasthan India Rajasthan Tech Univ Dept Comp Sci & Engn Kota Rajasthan India

ISBN: (纸本)9781538647691

The capabilities of four population based algorithms including multi-objective particle swarm optimization (MOPSO), improved non-dominated sorting genetic algorithm (NSGA-II), improved strength pareto evolutionary algorithm (SPEA2) and modified pareto envelop-based selection algorithm (PESA2) to acquire the solution for the multi-objective optimal power flow (Mo-OPF) problem are compared in this paper. For the Mo-OPF problem solution, the non-dominated solution sets are created by pareto optimal method. The best compromise solution among different solutions sets is chosen with the help of a fuzzy based decision mechanism. These operations are carried out on a standard IEEE 30-bus six-generator system subjected to system constraints and power flow balance. The load flow calculation is conducted with the help of iterative method. Total cost minimization of generation and total generation emission minimization are observed as desired function for optimal power flow (OPF) problem. In this paper, above algorithms solve Mo-OPF problem on the basis of operational feasibility, efficient operation, operational speed and best optimal solution for the given objectives.

关键词： Multi-objective particle swarm optimization improved non-dominated sorting genetic algorithm improved strength pareto evolutionary algorithm pareto envelope-based selection algorithm

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