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A Self-Learning Discrete jaya algorithm for Multiobjective Energy-Efficient Distributed No-Idle Flow-Shop Scheduling Problem in Heterogeneous Factory System

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 12675-12686页

作者： Zhao, Fuqing Ma, Ru Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

In this study, a self-learning discrete jaya algorithm (SD-jaya) is proposed to address the energy-efficient distributed no-idle flow-shop scheduling problem (FSP) in a heterogeneous factory system (HFS-EEDNIFSP) with the criteria of minimizing the total tardiness (TTD), total energy consumption (TEC), and factory load balancing (FLB). First, the mixed-integer programming model of HFS-EEDNIFSP is presented. An evaluation criterion of FLB combining the energy consumption and the completion time is introduced. Second, a self-learning operators selection strategy, in which the success rate of each operator is summarized as knowledge, is designed for guiding the selection of operators. Third, the energy-saving strategy is proposed for reducing the TEC. The energy-efficient no-idle FSP is transformed to be an energy-efficient permutation FSP to search the idle times. The speed of operations which adjacent are idle times is reduced. The effectiveness of SD-jaya is tested on 60 benchmark instances. On the quality of the solution, the experimental results reveal that the efficacy of the SD-jaya algorithm outperforms the other algorithms for addressing HFS-EEDNIFSP.

关键词： Production facilities Job shop scheduling Optimization Energy consumption Search problems Load management Sustainable development Energy-efficient distributed no-idle flow-shop scheduling problem (FSP) energy-saving strategy heterogeneous factory system jaya algorithm self-learning operation selection strategy (SLOS)

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Comprehensive learning jaya algorithm for engineering design optimization problems

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第5期33卷 1229-1253页

作者： Zhang, Yiying Jin, Zhigang Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

jaya algorithm (jaya) is a recently developed metaheuristic algorithm for global optimization problems. jaya has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, jaya is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this disadvantage of jaya, this paper presents an improved jaya, named comprehensive learning jaya algorithm (CLjaya), for solving engineering design optimization problems. The core idea of CLjaya is the designed comprehensive learning mechanism by making full use of population information. The designed comprehensive learning mechanism consists of three different learning strategies to improve the global search ability of jaya. To investigate the performance of CLjaya, CLjaya is first evaluated by the well-known CEC 2013 and CEC 2014 test suites, which include 50 multimodal test functions and eight unimodal test functions. Then CLjaya is employed to solve five real-world engineering optimization problems. Experimental results demonstrate that CLjaya can achieve better solutions for most test problems than jaya and the other compared algorithms, which indicates the designed comprehensive learning mechanism is very effective. In addition, the source code of the proposed CLjaya can be loaded from .

关键词： jaya algorithm Comprehensive learning Metaheuristic algorithm Engineering optimization

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An Intensive and Comprehensive Overview of jaya algorithm, its Versions and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022年第2期29卷 763-792页

作者： Abu Zitar, Raedal Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Doush, Iyad Assaleh, Khaled Sorbonne Univ Abu Dhabi Sorbonne Univ Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Abu Dhabi U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Amer Univ Kuwait Comp Dept Salmiya Kuwait Yarmouk Univ Comp Sci Dept Irbid Jordan

In this review paper, jaya algorithm, which is a recent population-based algorithm is intensively overviewed. The jaya algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of jaya algorithm are carefully analyzed. Thereafter, the proposed versions of jaya algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of jaya algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of jaya algorithm are identified to provide enrich resources for jaya research communities. The critical analysis of jaya algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of jaya algorithm. The reader of this overview will determine the best domains and applications used by jaya algorithm and can justify their jaya-related contributions.

关键词： jaya algorithm Metaheuristics Optimization Exploration Exploitation

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Structural damage identification considering uncertainties based on a jaya algorithm with a local pattern search strategy and L0.5 sparse regularization

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ENGINEERING STRUCTURES 2022年 261卷 1页

作者： Ding, Zhenghao Hou, Rongrong Xia, Yong Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China

This study develops an improved jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified jaya algorithm is developed by integrating the one-step K-means clustering, Hooke-Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard jaya algorithm, which is verified by a series of high-dimension test functions. An L-0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties.

关键词： Structural damage identification jaya algorithm Temperature variations L-0.5 regularization item Coupling effects

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Modified jaya algorithm for Machinability Assessment and Conflicting Response Optimization During Milling of Nanostructured Carbon Onions Reinforced Epoxy Composites

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EXPERIMENTAL TECHNIQUES 2022年第6期46卷 1007-1025页

作者： Kesarwani, S. Verma, R. K. Debnath, K. Madan Mohan Malaviya Univ Technol Dept Mech Engn Mat & Morphol Lab Gorakhpur 273010 Uttar Pradesh India Natl Inst Technol Dept Mech Engn Shillong 793003 Meghalaya India

Polymer composites are extensively accepted in the material fraternity due to their lightweight, high-strength quality. The carbon nanomaterials (CMNs) significantly enhance the thermal resistivity, anti-corrosion, mechanical, and physical features of polymer (epoxy) composites. This paper emphasizes the machining aspects of Zero-dimensional Carbon nano onion (0D-CNO) reinforced polymer composites. The influence of four varying constraints, namely, weight% of CNO, Spindle speed, feed rate, and depth of cut, was examined through the Grey integrated jaya algorithm (GRA- jaya). The desired value of Surface roughness (Ra) and Material Removal Rate (MRR) was controlled through process constraints. The Milling experimentation was executed through the Taguchi L-27 orthogonal array (OA). The conflicting machining performances were aggregated by Grey theory to develop the algorithm fitness function. To obtain a better work efficiency, higher spindle speed, feed rate, depth of cut, and lower weight% of nanomaterial addition are proposed in this study. Hence, the result demonstrated that the weight% of CNO reinforcement and feed rate are the most influential factors for optimal machining performance results. The finding of the proposed module has compared with other metaheuristics algorithms, which demonstrate the higher application potential to acquire the desired machining characteristics. It can be suggested for the enhancement of quality and productivity indices of the polymer manufacturing sector.

关键词： jaya algorithm Grey CNO Polymer Milling

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Multi-skill resource constrained project scheduling using a multi-objective discrete jaya algorithm

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APPLIED INTELLIGENCE 2022年第5期52卷 5718-5738页

作者： Li, Yang-Yuan Lin, Jian Wang, Zhou-Jing Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

Multi-skill resource constrained project scheduling problem (MS-RCPSP) is one of the widest investigated problems in manufacturing systems. This paper presents a multi-objective discrete jaya (MODjaya) algorithm to address the MS-RCPSP with the objectives of minimizing makespan and total cost simultaneously. In MODjaya, the best and worst solutions are used to generate offspring. Two simple heuristics are employed to enhance the exploration ability. An efficient encoding and decoding pair is designed to construct feasible schedules, and different assignment criteria are proposed to increase solution diversity. In addition, a left-shift scheme is embedded into the decoding process for improving resource utilization. The performance of the proposed algorithm is evaluated on an intelligent multi-objective project scheduling environment benchmark dataset. Computational results and comparisons with the state-of-the-art algorithms indicate the superiority of the MODjaya in solving the multi-objective MS-RCPSP.

关键词： Multi-objective jaya algorithm Multi-skill Project scheduling

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Modular energy management system with jaya algorithm for hybrid energy storage in electric vehicles

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第15期46卷 21497-21510页

作者： Demircali, Akif Koroglu, Selim Pamukkale Univ Dept Elect & Elect Engn TR-20160 Denizli Turkey

Increasing the energy efficiency in hybrid energy storage-based electric vehicles is among the important research areas. Energy sharing in such vehicles is possible with an efficient and optimum energy management system. In this study, a jaya-based modular energy management system is proposed to optimize power sharing between battery and ultracapacitor for an electric vehicle. In this system, battery is connected to DC bus directly, and ultracapacitor is connected via a bidirectional DC-DC converter. This converter provides the power flow between the DC bus and the ultracapacitor as well as perform the proposed energy management system algorithm. The designed modular energy management system has the feature of being easily integrated into a battery-only electric vehicle, and two-way power transfer between the battery and the ultracapacitor has been successfully achieved. At the same time, real-time implementation of jaya-based energy management system is experimentally tested for the first time and compared to rule-based energy management system. The proposed jaya-based energy management system reduces the total loss amount by 24.5% and the battery current root mean square value by 28.8% compared to the rule-based method. The results show that the proposed method can be successfully applied for real-time energy management system applications.

关键词： battery electric vehicle jaya algorithm modular energy management system ultracapacitor

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An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and jaya algorithm for parameter extraction of photovoltaic models

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 5610-5637页

作者： Zhang, Yujun Wang, Yufei Li, Shuijia Yao, Fengjuan Tao, Liuwei Yan, Yuxin Zhao, Juan Gao, Zhengming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Foreign Languages Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid jaya and Rao-1 algorithm, called EHRjaya, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRjaya has superior performance and rank in the leading position among the famous algorithms.

关键词： parameter extraction photovoltaic models jaya algorithm Rao-1 algorithm EHRjaya algorithm

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Powerful enhanced jaya algorithm for efficiently optimizing numerical and engineering problems

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SOFT COMPUTING 2022年第11期26卷 5315-5333页

作者： Gholami, Jafar Kamankesh, Mohamad Reza Mohammadi, Somayeh Hosseinkhani, Elahe Abdi, Somayeh Islamic Azad Univ Dept Comp Engn Kermanshah Sci & Res Branch Kermanshah Iran Islamic Azad Univ Dept Comp Engn Borujerd Branch Lorestan Iran Islamic Azad Univ Dept Comp Engn Kermanshah Branch Kermanshah Iran Islamic Azad Univ Dept Comp Engn Eslam Abade Gharb Branch Eslamabad E Gharb Iran FreieUniv Berlin Dept Math & Comp Sci Berlin Germany

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. jaya algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of jaya to present a high-efficiency version named Powerful Enhanced jaya (PEjaya). In other words, the methodology of updating position in jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of jaya, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine-cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations.

关键词： jaya algorithm Improvement Real-world evaluation Benchmark functions

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Multi-strategy jaya algorithm for industrial optimization tasks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4379-4393页

作者： Yu, Xiaobing Luo, Wenguan Rao, R. Venkata Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing Peoples R China SV Natl Inst Technol Dept Mech Engn Surat Gujarat India

jaya, a simple heuristic algorithm, has shown attractive features, especially parameter-free. However, the simple structure of jaya algorithm may result in poor performances, to boost the performance, a multi-strategy jaya (Mjaya) algorithm based on multi-population has been proposed in this paper. Three strategies correspond to three groups of solutions. The first strategy based on the first population is to introduce an adaptive weight parameter to the position-updating equation to improve the local search. The second strategy is based on rank-based mutation to enhance the global search. The third strategy is to exploit around the best solution to reinforce the local search. Three strategies cooperate well during the evolution process. The experimental results based on CEC 2014 have proven that the proposed Mjaya is superior compared with jaya and its latest variants. Then, the proposed Mjaya algorithm is used to solve three industrial problems and the results have shown that the proposed Mjaya algorithm can also solve complex industrial applications effectively.

关键词： jaya algorithm multi-strategy optimization adaptive

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