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A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites

APPLIED SOFT COMPUTING 2023年 133卷

作者： Wang, Zhenwu Zhang, Wenteng Guo, Yinan Han, Mengjie Wan, Benting Liang, Shangchao China Univ Min & Technol Dept Comp Sci & Technol Beijing 100083 Peoples R China China Univ Min & Technol Dept Intelligent Control & Robot Beijing 100083 Peoples R China Dalarna Univ Sch Informat & Engn S-79188 Falun Sweden Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China

multi-objective optimization problems (MOPs) that widely exist in real world concern all optimal solutions compromised among multiple objectives. Chicken swarm optimization algorithm derived from emergent behaviors of organisms provides an effective way for handling MOPs. To speed up convergence and improve uniformity of Pareto-optimal solutions, a multi-objective chicken swarm optimization algorithm based on dual external archives and boundary learning strategy (MOCSODABL) is proposed in this paper. Dual external archives are employed to distinguish and choose two types of elite solutions, with the purpose of more effectively guiding individual evolution. A boundary learning strategy guides the chickens to learn from boundary individuals in the later stage of evolution. Moreover, fast non-dominated sorting is adopted to establish the hierarchical social structure of a chicken population, and learning strategies of roosters, hens and chicks are improved to meet the requirements of MOPs. Experimental results on 14 benchmark functions show that the proposed MOCSO-DABL outperforms other five state-of-the-art algorithms significantly.& COPY;2022 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization problem Meta-heuristic Chicken swarm optimization Pareto dominance

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Optimal Placement of Data Concentrators and Repeaters in PLC-enabled Smart Grids

Optimal Placement of Data Concentrators and Repeaters in PLC...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Stiri, Souhaima Chaoub, Abdelaali Grilo, Antonio Bennani, Rachid Lakssir, Brahim Tamtaoui, Ahmed INPT MUSICS Team STRS Lab Rabat Morocco MAScIR D&MSD Lab Rabat Morocco Univ Lisbon IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781665435970

The focus of this paper is on Power Line Communication (PLC) technology which has a great potential to accommodate the huge amount of data produced by different applications and nodes in the context of Smart Grids (SGs). In particular, this paper dives deeper into the planning and placement of Data Concentrator (DC) in various territorial categories: urban, suburban and rural environments. Supplemental repeaters, usually omitted in previous stat-of-the-art, are inserted throughout the electrical grid to reach the entire network. A multi-criteria optimization approach has been followed and the popular NSGA-II algorithm has been exploited to find a good trade-off between the conflicting performance and profitability objectives. Results of extensive numerical simulation are provided confirming the effective need of repeaters especially in remote areas where Smart Meters (SMs) are sparsely distributed. Moreover and despite the destructive influence of fading and noise along with the involved investments, PLC is still able to provide a satisfactory balance between performance and cost.

关键词： Smart Metering PLC Data Concentrator and Repeater Placement multi-objective optimization problem multi-objective Evolutionary Algorithm

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multi-objective uplink data transmission optimization for edge computing in UAV-assistant mobile wireless sensor networks

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JOURNAL OF SYSTEMS ARCHITECTURE 2022年 132卷

作者： Li, Jiahui Sun, Geng Liang, Shuang Wang, Yuxin Wang, Aimin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Unmanned aerial vehicles (UAVs) have attracted growing attention in enhancing the performance of mobile wireless sensor networks (MWSNs) since they can act as aerial servers (ASs) and have the autonomous nature to collect data for edge computing. In this paper, we consider to construct a virtual antenna array (VAA) consists of mobile sensor nodes (MSNs) and adopt collaborative beamforming (CB) to achieve long-distance and efficient uplink data transmissions with the ASs. First, we formulate a low interference and high-performance uplink transmission multi-objective optimization problem (UTMOP) of the CB-based UAV-assisted MWSN to simultaneously improve the total transmission rates, suppress the total maximum sidelobe levels (SLLs) and reduce the total propulsion energy consumptions of MSNs by jointly optimizing the positions and excitation current weights of MSN-enabled VAA and the order of communicating with different ASs. Then, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with chaos initialization, average grade mechanism and hybrid-solution generate strategy to solve the problem. Simulation results verify that the proposed algorithm can effectively solve the formulated UTMOP, and it has better performance than some other benchmark methods and peer algorithms.

关键词： Mobile wireless sensor networks Unmanned aerial vehicles Collaborative beamforming Non-dominated sorting genetic algorithm-III multi-objective optimization problem

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An Improved multi objective Mayfly Algorithm for Solving Optimal Power Flow problem Considering Different Loading Conditions

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第10期31卷 2250177-2250177页

作者： Ramesh, S. Bhaskar, K. Vijaya Karunanithi, K. Ettappan, M. Chandrasekar, P. Raja, S. P. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Elect & Elect Engn Chennai 600062 Tamil Nadu India Chennai Inst Technol Dept Elect & Elect Engn Chennai 600069 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

This paper presents an improved multi-objective mayfly algorithm (IMOMA) to resolve the optimal power flow (OPF) problem in a regulated power system network with different loading conditions. The OPF problem, considered a multi-objective optimization problem, comprises multiple objective functions related to economic, technical, operational and security aspects. The IMOMA algorithm has been developed by implementing the simulated binary crossover (SBX), polynomial mutation and dynamic crowding distance (DCD) operators in the original multi-objective mayfly algorithm (MOMA).The OPF problem is analyzed by considering multiple objective functions in the IEEE30-bus test system, the IEEE118-bus test system and the 62-bus Indian utility system. The hypervolume performance metric is used to compare the performance of the MOMA and IMOMA with respect to different operating scenarios. Further, loading conditions ranging between 150% and 50% of the base load are considered for the evaluation. The effectiveness of the IMOMA over the MOMA is observed from the results of the different loads. The best compromise solution is obtained from a set of pareto optimal solutions by implementing the TOPSIS method.

关键词： Hypervolume mayfly algorithm multi-objective optimization problem optimal power flow TOPSIS

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Real-Time Drilling Parameter optimization Model Based on the Constrained Bayesian Method

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ENERGIES 2022年第21期15卷 8030-8030页

作者： Song, Jinbo Wang, Jianlong Li, Bingqing Gan, Linlin Zhang, Feifei Wang, Xueying Wu, Qiong Yangtze Univ Sch Petr Engn Wuhan 430100 Peoples R China Key Lab Drilling & Prod Engn Oil & Gas Wuhan 430100 Peoples R China CNPC Bohai Drilling Engn Co Ltd Engn & Technol Res Inst Tianjin 300457 Peoples R China CNPC Xibu Drilling Engn Co Ltd Urumqi 830011 Peoples R China PetroChina Tarim Oilfield Branch Korla 841000 Peoples R China

To solve the problems of the low energy efficiency and slow penetration rate of drilling, we took the geological data of adjacent wells, real-time logging data, and downhole engineering parameters as inputs;the mechanical specific energy and unit footage cost as multi-objective optimization functions;and the machine pump equipment limit as the constraint condition. A constrained Bayesian optimization algorithm model was established for the optimization solution, and drilling parameters such as weight-of-bit, revolutions per minute, and flowrate were optimized in real time. Through a comparison with NSGA-II, random search, and other optimization algorithms, and the application results of example wells, we show that the established Bayesian optimization algorithm has a good optimization effect while maintaining timeliness. It is suitable for real-time optimization of drilling parameters, can aid a driller in identifying the drilling rate and potential tapping area, and provides a decision-making basis for avoiding the low-efficiency rock-breaking working area and improving rock-breaking efficiency.

关键词： drilling parameter real-time optimization multi-objective optimization problem constrained Bayesian algorithm

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MOMRFO: multi-objective Manta ray foraging optimizer for handling engineering design problems

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KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107880-107880页

作者： Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Univ Mohamed Bachir Ibrahimi Dept Comp Sci Bordj Bou Arreridj Algeria Univ Setif 1 Dept Comp Sci Setif Algeria

Despite its recent appearance, manta ray foraging optimizer (MRFO) has shown a good ability to deal with single-objective real-world problems, which makes its application in solving problems with multiple objectives an interesting direction. Accordingly, the current paper investigates the MRFO optimizer to develop a new algorithm for handling multi-objective engineering design problems. To achieve this goal, the elitist concept is adopted to save the set of Pareto solutions through the integration of an external archive into the standard MRFO. This archive is considered also as a repository from which a search agent is chosen based on its density degree to control the convergence and diversity of manta rays population. Our algorithm's efficiency is first validated via extensive experiments on ten test functions, and the results were very satisfactory in terms of convergence and diversity, in almost all cases. Then, it is applied to four multi-objective engineering problems, and it showed a good promise in solving real-world problems with multiple objectives. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization problem Manta ray foraging optimizer Adaptive archive Convergence and diversity Engineering design problems

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multi-objective Layout optimization of Aircraft multi-branch Cable Harness Based on MOPSO/D 12

Multi-objective Layout Optimization of Aircraft Multi-branch...

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12th International Conference on Mechanical and Aerospace Engineering (ICMAE)

作者： Zhang, Dan Liu, Zhaochao Zhou, Chen Zuo, Dunwen Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China

ISBN: (纸本)9781665433211

The routing of multi-branch cable harness in aircraft relies heavily on manual work and experience, which is highly time-consuming. hi order to realize the automatic 3D layout of multi-branch cable harness, this paper presents a novel approach by solving the layout as a multi-objective optimization problem that is NP-hard. The optimization objectives are to minimize the weight of multi-branch cables, to maximize the proportion of main roads, and to maximize the openness of the wiring path. Various regulatory and functional design rules are considered to be constraints. And the number and spatial position of branch points are taken as decision variables. As a result, a multi-objective layout optimization problem model of multi-branch cable based on Steiner tree is established. Then an improved multi-objective particle swarm optimization based on decomposition (MOPSO/D) is proposed to obtain the Pareto solution set of multi-branch cable layout. Finally, the feasibility of the proposed method is verified by the cable layout of the aircraft cabin.

关键词： cable harness layout Steiner minimum tree multi-objective optimization problem MOPSO/D

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Revenue optimization modeling for renewable energy resource mix for sustainable development

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JOURNAL OF REVENUE AND PRICING MANAGEMENT 2021年第2期20卷 108-115页

作者： Gupta, Neha Agarwal, Mohini Garg, Pratibha Bansal, Manoj Amity Univ Uttar Pradesh Amity Sch Business Noida India

The adoption and attainment of sustainable development goals have diverted developing nations like India towards the use of renewable resource for meeting the growing need for electricity. With the advancement in technology for generating electricity, the concern is to develop such renewable energy mix that can help satisfy the electricity demand and be environment friendly. In this paper, one such problem wherein the three conflicting criterion such as maximization of energy savings, maximization of efficiency and minimization of the cost of installation has been considered for designing a multi-objective optimization model to meet the growing demand of electricity. Interactive fuzzy goal programming technique with three different functional forms of membership function namely linear, exponential, and hyperbolic have been used to solve the proposed problem. The results have shown substantial adequacy in meeting the desired load demand and at the same time reduction in installation cost has been seen which in turn will impact the revenue generation.

关键词： Energy efficiency Interactive fuzzy goal programming technique multi-objective optimization problem Renewable sources Revenue optimization

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Study on optimization of Interior Ballistic Performance of Cased Telescoped Ammunition Based on Improved Firefly Algorithm

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PROPELLANTS EXPLOSIVES PYROTECHNICS 2023年第2期48卷 e202200254-e202200254页

作者： He, Xin-Jia Li, Chao Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single-objective optimization and multi-objective optimization respectively. The results show that in the single-objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi-objective optimization, three sub-objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi-objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi-objective optimization algorithms, such as non-dominated sorting genetic algorithm version II (NSGA-II) and non-dominated sorting genetic algorithm version III (NSGA-III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent.

关键词： Cased telescoped ammunition Improved firefly algorithm multi-objective optimization problem Interior Ballistics Charge structure parameters

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Investigating Communications Energy Efficiency Tradeoff Between UAV Users and Small-cell Users 32

Investigating Communications Energy Efficiency Tradeoff Betw...

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32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC)

作者： Hashemi, Ramin Mili, Mohammad Robat Ali, Samad Beyranvand, Hamzeh Latva-aho, Matti Univ Oulu CWC Oulu Finland Islamic Azad Univ Tehran Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran

ISBN: (纸本)9781728175867

In this paper, a novel method is proposed to study the tradeoff between energy efficiency (EE) of small-cell users and unmanned aerial vehicles (UAV) users in multi-cell orthogonal frequency division multiple access (OFDMA)-based networks. Contrary to the prior works that only maximize the EE of the UAV network subject to some constraints on transmit power of UAV users, we formulate a multi-objective optimization problem (MOOP) that jointly maximize the EE of small-cell and UAV users while guaranteeing the minimum rate for UAV users as well as maximum transmit powers for the corresponding small-cell and UAV BSs. The proposed MOOP is transformed into a single optimization problem (SOOP) by the weighted Tchebycheff approach. Then, an iterative technique is used to optimize alternatively subchannels and transmission powers of small-cell and UAV networks at each step. Numerical results show that a substantial performance gain can be obtained over the existing solutions.

关键词： Energy efficiency heterogeneous network multi-objective optimization problem OFDMA small-cell unmanned aerial vehicles

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