Enhanced Evolutionary Search Algorithms for Multiobjective Optimization in Power System
نویسنده
چکیده
The development of electricity networks towards the future smart grids is naturally accompanied by increasing complexity of technical, economic and environmental problems. The new challenges require the development of new techniques and optimization methods, including specific approaches to multiobjective optimization problems. This paper focuses on basic and multiobjective optimization methods based on modern Evolutionary Computation (EC) metaheuristics inspired from the principle of Ordered Movement of Particles (OMP), such as Gravitational Search Algorithm (GSA) or the Charged System Search Algorithm (CSSA). The implementation of the proposed search algorithms is demonstrated for the case of a classical power systems problem, namely the optimal reactive power compensation using capacitor banks. Key-Words: Evolutionary Computation, Gravitational Search Algorithm, Multiobjective optimization, Pareto optimization, Non-dominant sorting, Optimal reactive power compensation.
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