In this paper, we present an efficient intelligent search algorithm for the two-dimensional rectangular strip packing problem. This algorithm involves three stages, namely greedy selection, local improvement, and rand...
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In this paper, we present an efficient intelligent search algorithm for the two-dimensional rectangular strip packing problem. This algorithm involves three stages, namely greedy selection, local improvement, and randomized improvement. The greedy selection stage provides a good initial solution for the local improvement stage, which then tries to improve the solution by a deterministic local search. The randomized improvement stage employs a simple randomized local search process, which does not need any control parameter. Each of these three stages uses a heuristic approach to construct solutions based on an improved scoring rule and the least-waste-first strategy. Extensive experiments show that, to the best of our knowledge, our proposed algorithm performs slightly better than all previously published metaheuristics for most of the benchmark instances.
Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an...
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Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an optimization problem of UMWF by considering the maintenance route and resources. To solve this problem, firstly, the Efficient Maintenance Value (EMF) is proposed as an objective function to measure the maintenance value, based on which a novel Random Variable Neighborhood Descent and Cuckoo search-based Hybrid Discretized Artificial Fish Swarm algorithm (RVNDCS-HDAFSA) is proposed to search the optimal maintenance strategy. Experiments are conducted to verify the local search capabilities of the hybrid algorithm, and extensive comparison experiments with the other algorithms are conducted at different scales to validate the effectiveness of the RVNDCS-HDAFSA algorithm;the result of the comparison experiments and the ANOVA shows that the HDAFSA demonstrates a superior capability in solving the medium-scale and large-scale problems compared to the existing algorithm. In conclusion, the practical application experiments validate the effectiveness of our proposed approach.
Permanent magnet arc motor (PMAM) is widely used in some scanning systems, such as the astronomical telescopes and large antennas. In order to address the optimization design of the PMAM, a new multi-objective optimiz...
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Permanent magnet arc motor (PMAM) is widely used in some scanning systems, such as the astronomical telescopes and large antennas. In order to address the optimization design of the PMAM, a new multi-objective optimization design method by combing gradient boosting decision tree (GBDT) and differential evolution algorithm (DEA) is proposed in this paper. Specifically, the motor structure of the PMAM is firstly presented. And then, a finite-element (FE) model of the PMAM is built to obtain sample data. Based on the sample data, the powerful machine learning algorithm called GBDT is innovatively introduced to establish surrogate model, which can identify the mapping relationships between the optimization objectives and the structural parameters. Subsequently, a popular intelligent search algorithm named DEA is employed to conduct the optimization design. Finally, both FE simulation and prototype experiment are performed to verify the effectiveness and advantages of the proposed method.
Chemical process optimization problems are often modeled as dynamic optimization problems (DOPs). Due to the nonlinear, multimodal and multi-dimensional nature of DOPs, efficient solution of DOPs is a very challenging...
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ISBN:
(纸本)9781728140940
Chemical process optimization problems are often modeled as dynamic optimization problems (DOPs). Due to the nonlinear, multimodal and multi-dimensional nature of DOPs, efficient solution of DOPs is a very challenging task. In this paper, a new intelligent search algorithm called symbiotic organisms search (SOS) is proposed for tackling the chemical DOPs. SOS mainly mimics three symbiotic relationships in ecosystem, namely mutualism, commensalism and parasitism, to perform global search. In addition, compared with previous intelligent search algorithms, SOS has the advantages of no tuning parameters, easy to implement and high performance. Combined with control vector parameterization, the proposed SOS is applied to solve five chemical DOPs with different levels of complexity. Simulation results show that SOS can achieve solutions with accuracy comparable to those methods in the literature, and thus can be regarded as an effective tool for the chemical DOPs.
The optimal allocation of distributed energy resources is one of the most important and challenging task toward realizing smart grid objectives. Smart grid initiatives may be realized after obtaining integrated soluti...
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The optimal allocation of distributed energy resources is one of the most important and challenging task toward realizing smart grid objectives. Smart grid initiatives may be realized after obtaining integrated solutions of distributed energy resources while taking into account the realistic operational strategy of distribution network reconfiguration. This article addresses improved variants of three meta-heuristic techniques-the improved genetic algorithm, improved particle swarm optimization, and improved teaching-learning based optimization-to efficiently handle the problem of simultaneous allocation of distributed energy resources, such as shunt capacitors and distributed generators in radial distribution networks. The problem is formulated to maximize annual energy loss reduction and to maintain a better node voltage profile while considering network reconfiguration under a variable load scenario. Several algorithm specific modifications are suggested in the standard forms of genetic algorithm, particle swarm optimization, and teaching-learning based optimization to overcome their intrinsic flaws. In addition, an intelligent search algorithm is proposed to further enhance the performance of optimizing techniques. The proposed methods are investigated on the benchmark IEEE 33-bus test distribution system, and a comparative analysis is carried out to judge the suitability of the proposed techniques. The application results obtained are promising when compared with other established methods.
Expert system has eveloped to crop breeding,fertilization,irrigation,diseases diagnosis,prevention and other *** central issues of the expert system is to solve the objectives optimization problems during the design p...
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Expert system has eveloped to crop breeding,fertilization,irrigation,diseases diagnosis,prevention and other *** central issues of the expert system is to solve the objectives optimization problems during the design process of agricultural disease expert system,with uncertainty and complexity as its natural instincts,expert system often depend upon intelligentsearch *** now,intelligent search algorithm has been extensively applicated in knowledge representation,search and inference of expert *** order to improve capability of global search in expert system,biogeography-based optimization(BBO) intelligent alogoration is introduced in this paper for characteristic of search mechanism,consequently,search mechanism of expert system based on BBO is *** has been proposed by Simon(2008),which works based on the two mechanisms;migration and mutation,like most of evolutionary algorithm,BBO has already proved its effectiveness as a commendable optimisation *** BBO,poor solutions accept a lot of new features from good ones which can help to improve the quality of those *** is a unique feature of *** superiority of the performance of BBO,compared to other evolutionary algorithms,has already been ***,it shares information amongst the solutions rather than does not involve reproduction,which clearly distinguishes it from reproductive strategies such as genetic algorithm(GA) and evolutionary strategies in which the solutions are lost at the end of each ***,BBO also clearly differs from ant colony optimization(ACO),because BBO maintains its set of solutions from one iteration to the next,and ACO generates a new set of solutions with each *** can be contrasted with particle swarm optimisation(PSO) and differential evolution(DE) in that BBO solutions are changed directly via migration from other solutions,PSO solutions do not change directly which first their velocities are changed,then po
Expert system has eveloped to crop breeding, fertilization, irrigation, diseases diagnosis, prevention and other *** central issues of the expert system is to solve the objectives optimization problems during the desi...
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Expert system has eveloped to crop breeding, fertilization, irrigation, diseases diagnosis, prevention and other *** central issues of the expert system is to solve the objectives optimization problems during the design process of agricultural disease expert system, with uncertainty and complexity as its natural instincts, expert system often depend upon intelligentsearch *** now,intelligent search algorithm has been extensively applicated in knowledge representation, search and inference of expert system.
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