seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understandin...
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seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, effectiveness of the SOA has been tested for optimized reactive power control of an isolated wind-diesel hybrid power system model. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for the purpose of power generation. IG offers many advantages. over the SG but it requires reactive power support for its operation. So, there is a gap between reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, a variable source of reactive power such as static VAR compensator (SVC) is used. The SG is equipped with IEEE type-I excitation system and dual input power system stabilizer (PSS) like IEEE-PSS3B. The performance analysis of a Takagi-Sugeno fuzzy logic (TSFL)-based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. In time-domain simulation of the investigated power system model, the proposed SOA-TSFL yields on-line, off-nominal coordinated optimal SVC and PSS parameters resulting in on-line optimal reactive power control and terminal voltage response. The performance of the proposed controller, with the influence of signal transmission delay, has also been investigated. (C) 2013 Elsevier B.V. All rights reserved.
seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized...
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seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized parameters in PEMFC polarization curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10^-23. Hence, the optimization model could fit the experiment data with high precision.
seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In S...
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seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimizationalgorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. Sugeno fuzzy logic (SFL)-based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters. (C) 2011 Elsevier Ltd. All rights reserved.
Human Group optimization (HGO) algorithm, derived from the previously proposed seeker optimization algorithm (SOA), is a novel swarm intelligence algorithm by simulating human behaviors, especially human searching/for...
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ISBN:
(纸本)9783642215155
Human Group optimization (HGO) algorithm, derived from the previously proposed seeker optimization algorithm (SOA), is a novel swarm intelligence algorithm by simulating human behaviors, especially human searching/foraging behaviors. In this paper, a canonical HGO with local search (L-HGO) is proposed. Based on the benchmark functions provided by CEC2005, the proposed algorithm is compared with several versions of differential evolution (DE) algorithms, particle swarm optimization (PSO) algorithms and covariance matrix adaptation evolution strategy (CMA-ES). The simulation results show that the proposed HGO is competitive or, even, superior to the considered other algorithms for some employed functions.
This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with...
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This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality. (C) 2010 Elsevier Ltd. All rights reserved.
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