Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency

Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch

International audienceThis paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure as a local search to explore the search space very effectively. The robustness of the proposed modified PSO tested on 40 generating units with prohibited zones and compared with recent hybrid global optimization methods. The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time compared with recent previous approaches

Hybrid optimization algorithm to solve the nonconvex multiarea economic dispatch problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA-TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA-TLBO based approach to provide a better solution.fi=vertaisarvioitu|en=peerReviewed

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm