A framework for self-tuning optimization algorithm

Yang, Xin-She; Deb, Suash; Loomes, Martin J.; Karamanoglu, Mehmet

doi:10.1007/s00521-013-1498-4

Cited by 77 publications

(39 citation statements)

References 16 publications

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“…Extensive studies employed various meta-heuristic optimization algorithms in several applications and for tuning different parameters [27][28][29]. The current study conducted the FPA to tune the LFC parameters of multi-area interconnected power system.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Jagatheesan¹,

Anand

Samanta

et al. 2016

Neural Comput & Applic

110

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This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional-integral-derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K p ), integral gain (K i ) and derivative gain (K d )] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Jagatheesan¹,

Anand

Samanta

et al. 2016

Neural Comput & Applic

110

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“…The scaling parameters ( = 1,2) are calculated by = | − |, where and are the lower and upper bound of the parameter . Diversity of solutions is controlled by the randomization parameter which needs to be reduced gradually during iterations so that it can vary with the iteration counter [63].…”

Section: Firefly Algorithm For Solving the Cwpmentioning

confidence: 99%

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

Stojanović

Brajević

Stanimirović

et al. 2017

Mathematical Problems in Engineering

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The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

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“…Second, the weights are generated randomly and substituted in Eq. (21) to evaluate the population. In addition, the most profitable location,…”

Section: Step 8: the Weighted Sum Phase For Updating The Diversitymentioning

confidence: 99%

A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

et al. 2016

Self Cite

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This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around B Gai-Ge Wang the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.

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A framework for self-tuning optimization algorithm

Cited by 77 publications

References 16 publications

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

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