Simulated annealing with local search-a hybrid algorithm for unit commitment

Purushothama, G K; Jenkins, Lawrence

doi:10.1109/tpwrs.2002.807069

Cited by 94 publications

(9 citation statements)

References 9 publications

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“…The flowchart of the implemented algorithm is illustrated in Figure 1 below. The HGA structure retains essentially the GA structure described in the previous section; however, after mutation and before replacing the individuals of the next generation it performs a local search in order to find better quality individuals in the current generation [22,23]. As already described in the GA, only individuals exhibiting improvement in the objective function are included in the new generation [24].…”

Section: Hybrid Genetic Algorithmmentioning

confidence: 99%

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Ali¹,

Padmanaban²,

Twala³

et al. 2017

Energies

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Abstract:In this paper, the approach focused on the variables involved in assessing the quality of a distributed generation system are reviewed in detail, for its investigation and research contribution. The aim to minimize the electric power losses (unused power consumption) and optimize the voltage profile for the power system under investigation. To provide this assessment, several experiments have been made to the IEEE 34-bus test case and various actual test cases with the respect of multiple Distribution Generation DG units. The possibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been verified. Finally, four algorithms were trailed: simulated annealing (SA), hybrid genetic algorithm (HGA), genetic algorithm (GA), and variable neighbourhood search. The HGA algorithm was found to produce the best solution at a cost of a longer processing time.

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Section: Hybrid Genetic Algorithmmentioning

confidence: 99%

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Ali¹,

Padmanaban²,

Twala³

et al. 2017

Energies

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“…Inspired by the natural relationships of groups of animals, swarm-based algorithms, such as Particle Swarm Optimization (PSO) [4][5][6], the Artificial Bee Colony algorithm [7], Bacterial Foraging Optimization (BFO) [8], Cat Swarm Optimization (CSO) [9,10], and Ant Colony Optimization (ACO) [11], among others, provided sufficient evidence of efficiency and effectiveness in finding the optimal solutions to complex optimization problems [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

et al. 2021

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This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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“…Various mathematical programming and heuristic. Various approaches include mathematical programming and heuristic approaches such as dynamic programming [2], neural networks [3], simulated annealing [4][5][6], evolutionary programming [7][8][9] constraint logic programming [10], genetic algorithm [11][12][13],Lagrangian relaxation [14][15][16], branch and bound [17], tabu search [18,19], particle swarm optimization [22][23][24][25][26] approaches have been devoted to solve the UCproblem.This paper considers the generation scheduling problem which includes wind power generation along with thermal generating stations. In this problem, by increasing the reserve requirements, the impacts of wind power generation are modeled when specifying the reserve inequality for this problem.…”

Section: Introductionmentioning

confidence: 99%

Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm

Saravanan¹,

Subramanian²,

Dharamalingam³

et al. 2016

IJCA

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Integrating wind power with any other energy source in power system has many operational and scheduling complications because of its inconsistent nature in the process ofwind forecasting. In this paper, a new metaheuristic optimization method named Grey Wolf Optimization algorithm is involved for solving the problem of generation scheduling (GS) to obtain best possible solution in power systems taking into account the load balance, reserve requirement, wind power availability constraints, inequality and equality constraints. The proposed GWO method is applied to a test system involves 40 conventional units and 2 wind farms. The system performance of GWO algorithm is establishedbyevaluating the results obtained for different number of trails and various iterationsfor five different populations. Calculation of the solution for different populations in the systemdiscloses that the best optimal scheduleachieved by Grey Wolf Optimization algorithm.

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Simulated annealing with local search-a hybrid algorithm for unit commitment

Cited by 94 publications

References 9 publications

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Electric Power Grids Distribution Generation System for Optimal Location and Sizing—A Case Study Investigation by Various Optimization Algorithms

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Generation Scheduling of Electricity with Integrated Wind-Thermal Units using Grey Wolf Optimization Algorithm

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