An Improved Tabu Search for Economic Dispatch with Multiple Minima

Lin, Whei-Min; Cheng, Fu-Sheng; Tsay, Ming-Tong

doi:10.1109/mper.2002.4311692

Cited by 183 publications

(108 citation statements)

References 18 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…For solving economic load dispatch various conventional methods like bundle method [3], non linear programming , mixed integer linear programming [4][5][6][7], dynamic programming [5], quadratic programming [6] , Lagrange relaxation method [8], network flow method [9], direct search method [10] are used to solve such problems. When compared with the conventional (classical) techniques [10][11], modern heuristic optimization techniques based on operational research and artificial intelligence concepts, such as evolutionary algorithms [12][13], simulated annealing [14,15], artificial neural networks [16][17][18], and taboo search [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints. ELD problem is non linear, non convex type with multiple local optimal point due to the inclusion of valve point loading effect, multiple fuel options with diverse equality and inequality constraints.…”

Section: Iintroductionmentioning

confidence: 99%

“…Thus the conventional methods have failed to solve such problems as they are sensitive to initial estimates and converge into local optimal solution and computational complexity. Modern heuristic optimization techniques based on operational research and artificial intelligence concepts, such as simulated annealing [14][15], evolutionary programming [13] , genetic algorithm, tabu search [19][20], neural network, particle swarm optimization provides better solution. The PSO originally developed by Eberhart and Kennedy in 1995 [25], is a population based stochastic algorithm.…”

Section: Iintroductionmentioning

confidence: 99%

“…The primary objective of ELD is to schedule the committed generating units output so as to meet the required load demand at minimum cost satisfying all unit and system operational constraints. The optimal ELD should meet load demand, generation limit, ramp rate, prohibited operating zone, [1,2] [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints. ELD problem is non linear, non convex type with multiple local optimal point due to the inclusion of valve point loading effect, multiple fuel options with diverse equality and inequality constraints.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

Khan¹,

Mahala²

2014

IJAREEIE

View full text Add to dashboard Cite

ABSTRACT:In this paper ,various inertia weight strategy particle swarm optimization is used to obtain the optimal power dispatch for 6 unit generator system with constraints satisfaction and minimizing the operating cost. The results are compared among classical Particle Swarm Optimization(CPSO),e1PSO, e2PSO methods. The numerical results affirmed the robustness and proficiency of proposed approach over other existing method.KEYWORDS: E1-PSO, E2-PSO,Economic load dispatch, PSO, Inertia weight, Prohibited operating zones. I.INTRODUCTIONThe economic load dispatch (ELD) of power generating units has always occupied an important position in the electric power industry. The primary objective of ELD is to schedule the committed generating units output so as to meet the required load demand at minimum cost satisfying all unit and system operational constraints. The optimal ELD should meet load demand, generation limit, ramp rate, prohibited operating zone, [1,2] [19,20] have been given attention by many researchers due to their ability to find an almost global optimal solution for ELD problems with operation constraints. ELD problem is non linear, non convex type with multiple local optimal point due to the inclusion of valve point loading effect, multiple fuel options with diverse equality and inequality constraints. Dynamic programming method is one of the approaches to solve the non-linear and discontinuous ELD problem, but it suffers from the problem of "curse of dimensionality" or local optimality. Thus the conventional methods have failed to solve such problems as they are sensitive to initial estimates and converge into local optimal solution and computational complexity. Modern heuristic optimization techniques based on operational research and artificial intelligence concepts, such as simulated annealing [14][15], evolutionary programming [13] , genetic algorithm, tabu search [19][20], neural network, particle swarm optimization provides better solution. The PSO originally developed by Eberhart and Kennedy in 1995[25], is a population based stochastic algorithm. The PSO is an evolutionary optimization tool of swarm intelligence field based on a swarm (population), where each member seen as a particle and each particle is a potential solution to the problem under analysis. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem, and implements the simulation of social behavior. PSO however, allows each particle to maintain a memory of the best solution that it has found and the best solution found in the particle's neighborhood is swarm. The main advantage of PSO algorithm is summarized as: simple concept, easy implementation, robustness to control parameters, and computational efficiency when compared with mathematical algorithms and other heuristic optimization techniques. PSO can be easily applied to nonlinear and non-continuous optimization problem.

show abstract

Section: Iintroductionmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

Khan¹,

Mahala²

2014

IJAREEIE

View full text Add to dashboard Cite

show abstract

“…Previously a number of derivative-based approaches including Lagrangian multiplier method have been applied to solve ELD problems [2]. Over the past few decades, as an alternative to the conventional mathematical approaches, many salient methods have been developed for ELD problem such as evolutionary programming [3], particle swarm optimization [4], Genetic algorithm [5], and improved tabu search [6]. Recently a new population based evolutionary algorithm has been invented by Simon, based on biogeography.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Technique Based Economic Load Dispatch Using Biogeography-Based Optimization Algorithm

upadhyay¹,

Singh²

2014

IJIRSET

View full text Add to dashboard Cite

This paper presents a biogeography-based optimization (BBO) algorithm to solve both convex and nonconvex economic load dispatch (ELD) problems of thermal plants. Lack of exploration capability of biogeographybased optimization (BBO) leads to slow convergence. The proposed methodology can take care of economic dispatch problems involving constraints such as transmission losses, valve point loading, multi-fuel options and prohibited operating zones. Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems; the BBO is based on two steps mutation and migration. The purpose of this work is to find out the advantages of application of the soft computing technique and BBO in particular to the economic load dispatch problem.

show abstract

“…Many method were proposed to solve this kind shortage, such as using a specific neighborhood definition which employs a block of jobs notion [6], employing a flexible memory system to avoid the entrapment in a local minimum and developing the ideal of "distance" to the fitness to accelerate optimization [7], processing a new parallel model for TS algorithm based on the crossover operator of genetic algorithms [8], and using three continuous stages in process like stage continuous tabu search (SCTS) algorithm [9]. Each stage in SCTS algorithm will produce a starting point for next stage.…”

Section: Introductionmentioning

confidence: 99%

A Modified Staged Continuous Tabu Search Algorithm

Wang¹,

Jiang²,

Qi³

et al. 2013

Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information

View full text Add to dashboard Cite

-Based on the staged continuous tabu search (SCTS) algorithm, a modified staged continuous tabu search (MSCTS) algorithm is proposed in this paper to improve the convergence, speed and robustness of tabu search (TS) algorithm. The improvements focus on the selection method of the neighborhoods in MSCTS algorithm. The generation of neighborhoods is guided by the multidimensional normal distribution function. In multidimensional normal distribution function, the mean value is the current optimal solution and the standard deviation is produced by the difference vector of the objective function at the current optimal solution. The range setting of neighborhood is different at different stage. 9 typical functions are used to test the performance of MSCTS and SCTS algorithm respectively. There are 5 indexes to evaluate the performance of both algorithms. The tests results show that MSCTS algorithm is good at dealing with the multivariate optimization problems. The calculated optimum solution for multi-variable function by MSCTS algorithm is about 5 to 17 times as near to the theoretical optimum solution as that of SCTS algorithm. As to the same test function, the calculation speed of MSCTS algorithm is about 3 to 17 times as many as that of SCTS algorithm. At the same time, the application of MSCTS algorithm is more extensive.Index Terms -Global optimization, Tabu search algorithm, Neighborhoods structure, Selection range I . IntroductionOptimization technology is widely applied in a variety of fields, such as engineering design, science analysis and financial data treatment. With computer technology getting increasingly powerful, optimization technology attracts more and more researchers' interests.Tabu search (TS) algorithm is one of crucial global optimization techniques. TS algorithm was first proposed by Professor Glover in 1986, from University of Colorado [1-2], for solving combinatorial optimization problems. The essential characteristic of TS algorithm is that several optimal solutions found temporarily, called taboo objects, are recorded by tabu list, which prevents these taboo objects can be found again in next several iterations. The existence of tabu list persecutes computer to turn to another direction to find better solutions, so as to jump out of local optimal effectively.Like other global optimization techniques, TS algorithm is applied in a variety of fields, too. In chemical engineering, TS algorithm was used to find good model parameters for metabolic flux analysis (MFA) problems [3]. This method was also reported to be used in facility layout to improve the efficiency of material handing within a manufacturing system [4]. In addition, TS algorithm was reported to be used for structural software testing [5].Although TS algorithm can get global optimum by tabu list, the probability of being trapped in a local optimum still exists. Many method were proposed to solve this kind shortage, such as using a specific neighborhood definition which employs a block of jobs notion [6], employing a flex...

show abstract

An Improved Tabu Search for Economic Dispatch with Multiple Minima

Cited by 183 publications

References 18 publications

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch

Soft Computing Technique Based Economic Load Dispatch Using Biogeography-Based Optimization Algorithm

A Modified Staged Continuous Tabu Search Algorithm

Contact Info

Product

Resources

About