An application of the current search (CS), one of the most efficient metaheuristic optimization search techniques, to design the PIDA (proportional-integral-derivative-accelerated) controllers is proposed in this pape...An application of the current search (CS), one of the most efficient metaheuristic optimization search techniques, to design the PIDA (proportional-integral-derivative-accelerated) controllers is proposed in this paper. The CS is applied to search for the optimum PIDA controller’s parameters. The obtained controllers are tested against nine benchmark systems collected by ?sstr?m and H?gglund considered as the hard-to-be-controlled plants and an automatic voltage regulator (AVR) system. As results, the optimum PIDA controllers can be successfully obtained by the CS and the responses of controlled systems are very satisfactory.展开更多
This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm posses...This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.展开更多
This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)—one of the most efficient metaheuristic optimization search techniques. Assembly lines ...This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)—one of the most efficient metaheuristic optimization search techniques. Assembly lines of a specific car factory considered as a case study are balanced by the ACS to optimize their energy resource management. The workload variance of the line is performed as the objective function to be minimized in order to increase the productivity. In this work, the ACS is used to address the number of tasks assigned for each workstation, while the sequence of tasks is assigned by factory. Three real-world assembly line balancing (ALB) problems from a specific car factory are tested. Results obtained by the ACS are compared with those obtained by the genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms. By using the ACS, the productivity can be increased and the energy consumption of the lines can be decreased significantly.展开更多
文摘An application of the current search (CS), one of the most efficient metaheuristic optimization search techniques, to design the PIDA (proportional-integral-derivative-accelerated) controllers is proposed in this paper. The CS is applied to search for the optimum PIDA controller’s parameters. The obtained controllers are tested against nine benchmark systems collected by ?sstr?m and H?gglund considered as the hard-to-be-controlled plants and an automatic voltage regulator (AVR) system. As results, the optimum PIDA controllers can be successfully obtained by the CS and the responses of controlled systems are very satisfactory.
文摘This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.
文摘This paper aimed to present the optimization of energy resource management in a car factory by the adaptive current search (ACS)—one of the most efficient metaheuristic optimization search techniques. Assembly lines of a specific car factory considered as a case study are balanced by the ACS to optimize their energy resource management. The workload variance of the line is performed as the objective function to be minimized in order to increase the productivity. In this work, the ACS is used to address the number of tasks assigned for each workstation, while the sequence of tasks is assigned by factory. Three real-world assembly line balancing (ALB) problems from a specific car factory are tested. Results obtained by the ACS are compared with those obtained by the genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms. By using the ACS, the productivity can be increased and the energy consumption of the lines can be decreased significantly.
基金国家自然科学基金项目(50707026)陕西省自然科学基础研究计划项目(2014JM7255)+3 种基金陕西省重点学科建设专项资金资助项目(5X1201)Project Supported by National Natural Science Foundation of China(50707026)Shaanxi Province Natural Science Basic Research Program(2014JM7255)Shaanxi Key Discipline Construction Special Funds(5X1201)
