Solution of Economic Load Dispatch Problem Using Lbest-Particle Swarm Optimization with Dynamically Varying Sub-swarms.
ABSTRACT This article presents an efficient optimization approach to solve constrained Economic Load Dispatch (ELD) problem using a ‘Lbest-Particle Swarm Optimization with Dynamically Varying Sub-swarms' (LPSO-DVS). The proposed method is found to give optimal results while working with constraints in the ELD, arising due to practical limitations like dynamic operation constraints (ramp rate limits) and prohibited zones and also accounts valve point loadings. Simulations performed over various systems with different number of generating units with the proposed method have been compared with other existing relevant approaches. Experimental results support the claim of proficiency of the method over other existing techniques in terms of robustness, fast convergence and, most importantly its optimal search behavior.
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Article: Evolutionary programming made faster[Show abstract] [Hide abstract]
ABSTRACT: Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a “fast EP” (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems testedIEEE Transactions on Evolutionary Computation 08/1999; · 5.55 Impact Factor
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ABSTRACT: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functionsIEEE Transactions on Evolutionary Computation 03/2002; · 5.55 Impact Factor
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ABSTRACT: Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.IEEE Transactions on Evolutionary Computation 03/2003; · 5.55 Impact Factor