Global Journal of Technology and Optimization

Each of us for the first time after seeing the formula mass-energy equivalence E = m0c2 came to the admiration and amazement of how mass any of the body becomes energy. The answer to this question is absent both in the theory of relativity and in quantum mechanics. Once my colleague Edgars Alksnis noticed that New Cartesian physics will have success if explain the formula the mass-energy equivalence. It’s easy to make - thought I, at my was already formulated the law of constancy of flow of forces through a closed surface, which was a generalization of the Gauss law, according to which each movement begins as a result of deviations from this permanence.

Optimization includes finding best available values of some objective function given a defined domain. Function optimization (FO) is the well-studied continuous optimization task which aim is to find best suited parameter values to get optimal value of a function. A number of techniques have been investigated in last few decades to solve FO and recently Swarm Intelligence (SI) methods, imitating power of the collective behavior of insects or animals, become popular to solve it. A number of SI methods have been developed on different time and tested on different test functions; therefore, it is important to compare the algorithms on a common test bench to identify their capability as well as best suited method for FO. The objective of this study is to draw a fair comparison among prominent SI methods in solving benchmark test functions. The SI methods considered in this study are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) optimization, Firefly Algorithm (FFA), Cuckoo Search Optimization (CSO), Group Search Optimization (GSO) and Grey Wolf Optimizer (GWO). Among the methods PSO is the pioneer and most popular in recent time; and GWO is the most recently developed method. The performance of the methods is compared in solving a suite of 22 well known benchmark test functions having different ranges, dimensions and types. Experimental results as well as analysis revealed that GWO is the overall best method among the SI methods and PSO is still promising to solve bench mark functions.

This paper introduces a new method to solve economic dispatch problem in power system operation with wind farm (WF) connecting. The method is cuckoo search algorithm (CSA) which can to solve effective ED problem. The subject of this paper is optimal solutions about total power output each generator and WF with minimum operation cost in power system. The research study CSA and develop this method which become new method more efficient than former methods. CSA apply to solve ED problem with WF which give the best results and programming time. For ED problem simulator, program applies 30 buses IEEE system and Matpower 4.1 Toolbox to run power system with WF connecting. The results of this method also are compared to other previous methods and assess its results.