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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2020年第1期2卷 14-21页

作者： Gu, Tao Li, Sujian Kou, Zhenzhen Wu, Xiuli Univ Sci & Technol Beijing Sch Mech Engn 30 Xueyuan Rd Beijing Peoples R China

Aiming at the low quality of the solution obtained with the traditional marginal analysis algorithm for solving multi-level inventory optimisation problem of repairable spare parts, a hybrid algorithm integrating particle swarm optimisation, marginal analysis algorithm-METRIC model and tabu search is proposed. In the early stage of the hybrid algorithm, particle swarm optimisation is used to explore the global solution space and in the later stage, tabu search is used to local search, and the marginal analysis algorithm is used to constructing neighbourhood. The experimental results show that the proposed algorithm can solve the problem of spare parts multi-level inventory optimisation effectively and efficiently.

关键词： maintenance engineering inventory management particle swarm optimisation search problems optimisation hybrid optimisation algorithm multilevel spare parts inventory optimisation problem traditional marginal analysis algorithm multilevel inventory optimisation problem repairable spare parts hybrid algorithm particle swarm optimisation marginal analysis algorithm-METRIC model global solution space local search spare parts multilevel inventory optimisation

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Method for replica selection in the Internet of Things using a hybrid optimisation algorithm

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IET COMMUNICATIONS 2019年第17期13卷 2820-2826页

作者： Wakil, Karzan Nazif, Habibeh Panahi, Sepideh Abnoosian, Karlo Sheikhi, Saeid Sulaimani Polytech Univ Res Ctr Sulaimani 46001 Kurdistan Regio Iraq Payame Noor Univ Dept Math Tehran Iran Islamic Azad Univ Ardabil Branch Comp Engn Ardebil Iran Islamic Azad Univ Sci & Res Branch Dept Stat Tehran Iran Islamic Azad Univ Gorgan Branch Dept Comp Gorgan Golestan Iran

Internet of Things (IoT) as a new technological revolution has been proposed recently wherein the things are connected over the Internet. Because of the inherent characteristics of IoT for storage of data at untrusted and heterogeneous hosts, data replication across large geographic distances for efficient data management is unavoidable. The selection of appropriate replication things in the IoT, which reduces response time and cost is one of the most important issues of data management. Since this problem is an NP-hard problem, classic approaches are not efficient to solve this issue, and evolutionary algorithm such as ant colony optimisation (ACO) and genetic algorithm (GA) seems to be very useful. This study offers a method based on a combination of ACO and a GA to solve this problem. In the proposed method, the ACO has been used to create diversity, and afterwards, the GA is performed to provide a full search over the search space. The obtained results have shown the better performance of the proposed method in comparison with ACO, the High-QoS First-Replication (HQFR), and the Response Time-based Replica Management algorithms with regard to waiting time. In addition, the obtained results have revealed the better performance of the hybrid method in comparison with HQFR and the Dynamic Cost-aware Re-replication and Re-balancing Strategy.

关键词： ant colony optimisation genetic algorithms quality of service Internet of Things data handling waiting time hybrid method replica selection hybrid optimisation algorithm IoT technological revolution untrusted hosts heterogeneous hosts data replication geographic distances efficient data management appropriate replication things NP-hard problem classic approaches evolutionary algorithm ACO genetic algorithm GA dynamic cost-aware rereplication high-QoS first-replication response time-based replica management algorithms

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Coupling matrix synthesis of cross-coupled microwave filters using a hybrid optimisation algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2009年第6期3卷 950-958页

作者： Nicholson, G. L. Lancaster, M. J. Univ Birmingham Dept Elect Elect & Comp Engn Birmingham B15 2TT W Midlands England

A hybrid optimisation algorithm that synthesises coupling matrices for cross-coupled microwave filters is presented. A binary encoded genetic algorithm is combined at regular intervals with a sequential quadratic programming local search method to form a hybrid, exploiting the speed of the local search, while maintaining diversity with the genetic algorithm. The genetic algorithm uses the stochastic uniform selection technique and a multiple point crossover operator. A compact, efficient cost function requiring only the determinant and a cofactor of the coupling matrix is used as the basis of the optimisation algorithm. optimisation algorithms simplify the process of synthesising coupling matrices, compared with analytical synthesis. However, algorithms that use only local search methods cannot be guaranteed to find a global minimum. This hybrid method aims to extend the range of coupling matrices that can be synthesised by optimisation, while maintaining the speed of search. A coupling matrix for a tenth order coupling matrix for a dual band symmetric filter and a seventh order asymmetric filter are synthesised to verify the method.

关键词： binary encoded genetic algorithm hybrid optimisation algorithm dual band symmetric filter microwave filters resonator filters Passive filters and other passive networks stochastic uniform selection technique multiple point crossover operator seventh order asymmetric filter coupling matrix synthesis optimisation techniques sequential quadratic programming local search method cross coupled microwave filter Waveguide and microwave transmission line components genetic algorithms quadratic programming

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hybrid optimisation algorithm and its application for pattern synthesis of planar arrays

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JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6612-6615页

作者： Qi, Zhengdong Lu, Yangyi Kong, Yue Cong, Youji Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Jiangsu Peoples R China Inst CSIC Nanjing Jiangsu Peoples R China

A hybrid approach to the synthesis of sparse arrays radiating shaped or focused beampattern is presented in this paper. Specifically, an iterative scheme is used where the prescribed pattern response in the mainlobe peak direction is cast as a multi-convex problem at each step that the non-convex lower bound constraint is relaxed while including a reweighted l(1)-norm minimisation based on the magnitudes of the elements. Numerical tests, referred to benchmark problems, show that the proposed method achieves improved radiation performance by using fewer number of radiating elements.

关键词： minimisation iterative methods antenna radiation patterns planar antenna arrays convex programming hybrid optimisation algorithm pattern synthesis hybrid approach sparse arrays beampattern iterative scheme mainlobe peak direction multiconvex problem nonconvex lower bound constraint numerical tests benchmark problems radiation performance radiating elements pattern response planar atenna arrays reweighted l1 -norm minimisation

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Planning Method for Charging Station Based on the hybrid optimisation algorithm 3

Planning Method for Charging Station Based on the Hybrid Opt...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Feng, Jiangpeng Chang, Xiqiang Fan, Yanfang College of Electrical Engineering Xinjiang University Xinjiang Urumqi830047 China State Grid Xinjiang Electric Power Supply Company Xinjiang Urumqi830011 China

ISBN: (纸本)9798350382426

In light of the uneven development of charging infrastructure caused by the growing quantity of electric vehicles, a charging station planning method is proposed to comprehensively consider the interests of charging stations, electric vehicle subscribers, and distribution networks. Firstly, the charging station planning model is constructed, and the objective function is constructed with the target of minimising the charging station construction cost, user loss cost, and distribution network loss cost. Secondly, to solve the issues with the current algorithms' low efficiency and insufficient global optimisation capabilities, a hybrid optimisation algorithm based on the Differential Evolutionary (DE) and the Improved Particle Swarm Optimization (IPSO) is adopted to resolve the proposed planning model, and to identify the service scope, optimum number and location of charging stations. Finally, by using part of an urban region in a city in northern China as a case for planning purposes, the proposed algorithm and model are validated to be effective and feasible. © 2023 IEEE.

关键词： Charging station Charging station planning Electric vehicles hybrid optimisation algorithm

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hybrid APSO-spiral dynamic algorithms with application to tuning of filtered PPI controller in a wirelessHART environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 597-610页

作者： Hassan, Sabo Miya Ibrahim, Rosdiazli Saad, Nordin Asirvadam, Vijanth Sagayan Bingi, Kishore Abubakar Tafawa Balewa Univ Dept Elect & Elect Engn Bauchi Nigeria Univ Teknol Petronas Dept Elect & Elect Engn Bandar Seri Iskandar Perak Malaysia

The accelerated particle swarm optimisation (APSO) is an improved variant of the PSO algorithm that guarantees convergence through the use of only global best to update both velocity and position of particles. However, like its predecessor, the APSO is also prone to being trapped in local minima. Therefore, this paper proposes two hybrid algorithms synergizing the social ability of the APSO and the exploitative ability of both spiral dynamic algorithm (SDA) and Adaptive SDA(ASDA). The exploration phase of the proposed algorithms APSO-SDA and APSO-ASDA, will be achieved through the APSO algorithm. The exploration phase solutions of the APSO are then fed to the SDA and ASDA to achieve the exploitation phase. The proposed algorithms have been evaluated with benchmark function and have also been used to tune a filtered predictive proportional-integral (FPPI) controller for WirelessHART networked control systems (WHNCS). The results obtained from Friedman's rank test show that the proposed APSO-SDA and APSO-ASDA outperformed their constituent algorithms. Time domain analysis of the FPPI controller also show that the APSO-SDA and APSO-ASDA outperformed the APSO, SDA and ASDA in terms of settling times and overshoot.

关键词： Accelerated PSO hybrid optimisation algorithm predictive PI controller spiral dynamic algorithm WirelessHART

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M-LionWhale: multi-objective optimisation model for secure routing in mobile ad-hoc network

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IET COMMUNICATIONS 2018年第12期12卷 1406-1415页

作者： Chintalapalli, Ram Mohan Ananthula, Venugopal Reddy CVR Coll Engn Dept Comp Sci & Engn Hyderabad 500044 Telangana India Jawaharlal Nehru Technol Univ Hyderabad 500085 Telangana India

Mobile ad-hoc network (MANET) is an emerging technology that comes under the category of wireless network. Even though the network assumes that all its mobile nodes are trusted, it is impossible in the real world as few nodes may be malicious. Therefore, it is essential to put forward a mechanism that can provide security by selecting an optimal route for data forwarding. In this study, a goal programming model is designed using a hybrid optimisation algorithm, called M-LionWhale, for secure routing. M-LionWhale is an optimisation algorithm that incorporates lion algorithm (LA) into whale optimisation algorithm (WOA) for the optimal selection of the path in MANET. The multi-objective optimisation model considers several quality of service (QoS) parameters, namely energy, distance, link lifetime, delay, and trust. With the estimated multi-objective parameters, a fitness function is developed for the best selection of routes. The performance of the proposed algorithm is evaluated using three metrics, such as packet delivery ratio (PDR), throughput, and energy and is compared with that of existing trust-based QoS routing model, LA, and WOA. The proposed M-LionWhale algorithm could attain the maximum performance with 24.1313% residual energy, throughput of 0.2966kbps, and PDR of 0.3051 at maximum simulation time.

关键词： mobile ad hoc networks telecommunication security routing protocols mobile radio quality of service optimisation ad hoc networks telecommunication network routing secure routing lion algorithm whale optimisation algorithm optimal selection MANET multiobjective optimisation model multiobjective parameters QoS routing model M-LionWhale algorithm ad-hoc network Mobile emerging technology wireless network mobile nodes optimal route data forwarding goal programming model hybrid optimisation algorithm called M-LionWhale

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Design of multiple-passband filters using coupling matrix optimisation

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IET MICROWAVES ANTENNAS & PROPAGATION 2012年第1期6卷 24-30页

作者： Shang, X. Wang, Y. Nicholson, G. L. Lancaster, M. J. Univ Birmingham Sch Elect Electon & Comp Engn Birmingham B15 2TT W Midlands England

This study presents the design of microwave filters based on the coupling matrix approach;determination of the matrix is based on a hybrid optimisation algorithm which may be applied to cross-coupled filters having diverse topologies. Various filter responses from dual-band to quad-band are given as examples of the approach. The optimisation is performed on the coupling matrix and a genetic algorithm (GA) is employed to generate initial values for the control variables for a subsequent local optimisation (sequential quadratic programming search). The novel cost function in this study measures the difference of the frequency locations of reflection and transmission zeros between the response produced by the coupling matrix and the ideal response. The ideal response in the form of characteristic polynomials is determined from the filter specifications and generated by a recently developed iterative technique which is capable of realising multi-band filters with different return loss levels. Convergence of the coupling matrix optimisation is fast, and no initial values for the control parameters are required by the GA. An eighth-order X-band dual-band waveguide filter with all-capacitive-coupling irises has been fabricated and measured to verify the design technique. Excellent agreement between simulation and experimental result has been achieved.

关键词： hybrid optimisation algorithm sequential quadratic programming search genetic algorithm microwave filters coupling matrix optimisation band-pass filters multiple-passband filters quadratic programming Filters and other networks cross-coupled filters eighth-order X-band dual-band waveguide filter optimisation techniques Waveguide and microwave transmission line components waveguide filters multiband filters subsequent local optimisation

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Cost optimisation of reinforced concrete flat slab buildings

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ENGINEERING STRUCTURES 2005年第3期27卷 313-322页

作者： Sahab, MG Ashour, AF Toropov, V Univ Bradford EDTI Sch Engn Design & Technol Bradford BD7 1DP W Yorkshire England Amirkabir Univ Technol Dept Civil & Environm Engn Tafresh Branch Tehran Iran Altair Engn Ltd Vanguard Ctr Coventry CV4 7EZ W Midlands England

Cost optimisation of reinforced concrete flat slab buildings according to the British Code of Practice (BS8110) is presented. The objective function is the total cost of the building including the cost of floors, columns and foundations. The cost of each structural element covers that of material and labour for reinforcement, concrete and formwork. The structure is modelled and analysed using the equivalent frame method. The optimisation process is handled in three different levels. In the first level, the optimum column layout is achieved by an exhaustive search. In the second level, using a hybrid optimisation algorithm, the optimum dimensions of columns and slab thickness for each column layout are found. In this hybrid algorithm, a genetic algorithm is used for a global search, followed by a discretised form of the Hook and Jeeves method. In the third level, an exhaustive search is employed to determine the optimum number and size of reinforcing bars of reinforced concrete members. Cost optimisation for three reinforced concrete flat slab buildings is illustrated and the results of the optimum and conventional design procedures are compared. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： flat slab buildings reinforced concrete structural optimisation genetic algorithm hybrid optimisation algorithm

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Optimal allocation of plug-in electric vehicle capacity to produce active, reactive and distorted powers using differential evolution based artificial bee colony algorithm

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第8期11卷 1058-1070页

作者： Farahani, Hassan Feshki Rashidi, Farzan Islamic Azad Univ Ashtian Branch Dept Engn Ashtian Iran Islamic Azad Univ Cent Tehran Branch Dept Engn Tehran Iran Univ Hormozgan Elect & Comp Engn Dept Bandar Abbas Iran

Plug-in electric vehicles (PEVs) can produce active, reactive and distorted power as well as pollution reduction. This study proposes an optimisation framework to allocate the PEVs capacity to generate each power component considering to grid and vehicle constraints, technical concerns and market price. In the proposed framework, PEVs compete with active power-line conditioners (APLCs) to generate distorted power and with generators to produce active and reactive power. An objective function is defined which includes distribution system operator (DSO) payment for each market participant. This function is minimised based on a hybrid optimisation algorithm (HOA) combining artificial bee colony (ABC) and differential evolution (DE) algorithms subject to grid and vehicles constraints. In the presented algorithm, a novel self-adaptive modification phase is proposed to improve overall ability of the algorithm for optimisation applications. The effectiveness and efficiency of the method is demonstrated on a low-voltage network with 134 customers as a case study.

关键词： electric vehicles optimisation electric vehicle charging plug-in electric vehicle capacity differential evolution based artificial bee colony algorithm optimisation framework active power-line conditioners distribution system operator hybrid optimisation algorithm

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