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A fractional-order jaya algorithm with memory effect for solving global optimization problem

EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

作者： Peng, Yuexi Sun, Shiren He, Shaobo Zou, Juan Liu, Yuan Xia, Yizhang Xiangtan Univ Sch Comp Sci Xiangtan 411105 Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Xiangtan Univ Sch Automat & Elect Informat Xiangtan 411105 Peoples R China

The jaya algorithm is a widely used metaheuristic algorithm developed recently for solving global optimization problems. However, its simple search mechanism, which relies solely on the current best and worst individuals in the population, can easily lead to suffering from premature stagnation and getting stuck in local optimum when dealing with certain complex problems. To address this problem, this paper designs a novel fractional-order search strategy based on jaya algorithm with memory effect, called fractional-order jaya (Fjaya). The Gr & uuml;nwald-Letnikov fractional-order difference with the characteristics of long-term memory and non-locality is integrated into the jaya algorithm. Specifically, two local attractors are generated using the best and worst individuals as well as the average position of the current population. These two local attractors then undergo fractional-order transformations to extract historical information from both the current and previous iterations. Thus, Fjaya does not solely rely on information from the current best and worst individuals, but rather effectively leverages more historical information. To validate the performance of the proposed algorithm, it is tested on 18 well-known benchmark functions. Furthermore, Fjaya is applied to solve the parameter identification on four discrete chaotic systems. The experimental results demonstrate that Fjaya enhances the search capability when facing complex optimization problems, and it also exhibits satisfying parameter identification capability for chaotic systems.

关键词： Fractional-order difference Gr & uuml nwald-Letnikov definition jaya algorithm Parameter identification Discrete chaotic system

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SUSTAINABLE MULTI-DEPOT MULTIPLE TRAVELLING SALESMAN PROBLEM BY ROBUST MULTI OBJECTIVEQUASI OPPOSITIONAL jaya algorithm: A CASE STUDY OF SURAT CITY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第4期21卷 2885-2924页

作者： Bajaj, Aaishwarya Dhodiya, Jayesh SV Natl Inst Technol Dept Math Surat 395007 Gujarat India

. It is imperative to mitigate carbon emissions in transportation, given the uncertainties linked to variables like vehicle type, travel paths, vehicle weight, and velocity. This research tackles the sustainable multi-depot multiple traveling salesman problem (MDmTSP) by utilizing robust reference points and the aspiration level-based MOQO jaya algorithm (RPAL-based MOQO jaya). The MDmTSP presents challenges with multiple salespersons returning to depots, resulting in travel expenses, time risks, and carbon emissions uncertainties. To address this, two frameworks, namely expected value (EV) and optimistic value (OV), are employed, with carbon emissions and risk serving as constraints. This study highlights a practical case study in Surat City, exploring scenarios involving 10 and 50 city nodes and selecting depots based on city distances and road conditions. Notably, road conditions significantly impact vehicle selection, route planning, and speed adjustments, influencing the data generation for Surat City. The RPAL-based MOQO jaya outperforms alternative methods, such as AL-based MOQO jaya, Hybrid GA, Rao algorithms, fuzzy programming technique, and the CPLEX optimization tool by giving multiple alternative solutions to the decision-makers within less runtime. Additionally, sensitivity analysis was conducted concerning confidence levels, demonstrating the algorithm's adaptability in solving problems related to supply chain management, ship routing, and the vehicle routing problem (VRP).

关键词： Multi Depot multiple travelling salesman problem Carbon emission jaya algorithm Reference point Uncertain Environment

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Improved jaya algorithm for energy-efficient distributed heterogeneous permutation flow shop scheduling

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-24页

作者： Zhang, Qiwen Zhen, Tian Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Most existing studies on distributed permutation flow shop scheduling assume identical shops, overlooking the impact of heterogeneous shops. This paper addresses the energy-efficient distributed heterogeneous permutation flow shop scheduling problem, which accounts for variations in energy consumption when jobs are processed on different machines in heterogeneous factories. Several research gaps are stated following: (1) Previous studies predominantly use the NEH algorithm and its variants, which require substantial computational resources, while a single random initialization strategy fail to generate a high-quality population. (2) The classical jaya algorithm relies on a single learning target, which may lead the population to converge prematurely to local optima. (3) Confidence-based operator selection models are influenced by historical performance and cannot dynamically adjust operator weights based on recent performance. (4) The previous works lack of effective energy-saving strategies. To address these issues, we propose a competitive multilevel jaya algorithm with SPA-based multi-directional local search (CMJA-SPALS). Key innovations of CMJA-SPALS as follows: (1) A hybrid initialization strategy that generates a high-quality initial population with good diversity and convergence using fewer evaluations. (2) The multilevel competition mechanism uses non-dominated sorting to divide the population into multiple levels. Individuals within the same level are randomly paired for competition to determine diversified learning targets, significantly enhancing population diversity and reducing the risk of converging to local optima. (3) Individuals apply specific search operators based on its optimization bias, while the surprisingly popular algorithm (SPA) dynamically adjusts the selection probabilities of operators, improving local search success rates and accelerating convergence. (4) A critical path-based energy-saving strategy designed to reduce machine idle time

关键词： Permutation flow shop Energy-efficient distributed heterogeneous factory jaya algorithm Surprisingly popular algorithm Multi-objective optimization

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A hybrid evolution jaya algorithm for meteorological drone trajectory planning

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APPLIED MATHEMATICAL MODELLING 2025年 137卷

作者： Yang, Jie Liu, Jun Liu, Jingsen Shanxi Meteorol Informat Ctr Taiyuan 030006 Peoples R China Henan Univ Coll Software Kaifeng 475004 Peoples R China Henan Univ Henan Int Joint Lab Intelligent Network Theory & K Kaifeng 475004 Peoples R China

Aiming at the problems of unreasonable search range and low optimization performance in meteorological drone trajectory planning under complex obstacle threat environments, as well as the shortcomings of sometimes low and unstable optimization accuracy of the basic jaya algorithm and easy to fall into local optima, a meteorological drone trajectory planning method based on multi-strategy improvement jaya algorithm optimization is proposed. In order to meet the practical applications, the performance index trajectory planning model based on the weight coefficient method with the spherical coordinate system is established using the shortest trajectory, the minimum threat, the flight altitude, and the flight angle as the performance indexes, as well as the obstacles as the constraints. The simulation results of the improved algorithm for its solution are given, and the performance is compared with other heuristic algorithms. The results show that the planned path can be safer and more effective in avoiding hazardous sources by comprehensively considering the performance of the meteorological drone. Compared with other algorithms, the improved algorithm performs well in terms of searching accuracy and stability and generates the higher-quality trajectory.

关键词： Meteorological drone Weight coefficient method Spherical coordinate system Trajectory planning jaya algorithm

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A New Approach to Efficiency Measurement: Hybrid jaya algorithm and Data Envelopment Analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Koyuncuoglu, Mehmet Ulas Yesilyurt, Muhammet Ensar Yesilyurt, Filiz Akbas Sahin, Emre Elbi, Mehmet Dogan Pamukkale Univ Dept Management Informat Syst Kinikli Campus TR-20160 Denizli Turkiye Pamukkale Univ Dept Econ Kinikli Campus TR-20160 Denizli Turkiye Pamukkale Univ Dept Elect Elect Engn Kinikli Campus TR-20160 Denizli Turkiye

Efficiency measurement using linear programming is a crucial decision-making problem in operational research. Assessing efficiency scores via Stochastic Frontier Analysis (SFA), a parametric method, becomes challenging when decision-making units (DMUs) have multiple outputs, limiting the information's comprehensiveness. To address this issue, we propose an innovative alternative method that estimates SFA for DMUs with multiple outputs by defining multiple outputs as single outputs that yield the same efficiency. Combining DEA with heuristic methods like the jaya algorithm offers a novel approach for converting multiple outputs into a single output when inputs and efficiency scores are provided. The jaya algorithm used in this study includes two stopping criteria: convergence to a specified correlation value and reaching the maximum number of iterations. The proposed method successfully calculates the efficiency of DMUs for datasets of 20, 50, 97, and 100 units with correlation values of 95% and 99%. The jaya algorithm computed 20, 50, 97, and 100 DMU samples in approximately 0.04, 0.45, 3.96, and 3.22 h, respectively (target correlation values of 95%). This method facilitates faster and more accurate efficiency measurements and enhances the ability to handle datasets with multiple outputs, providing a more robust and comprehensive analysis in operational research.

关键词： Data Envelopment Analysis jaya algorithm Efficiency Convergence Single Output

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A jaya algorithm based on self-adaptive method for parameters identification of photovoltaic cell and module

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-26页

作者： Feng, Zhiyu Zhu, Donglin Guo, Huaiyu Xue, Jiankai Zhou, Changjun Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Zhejiang Peoples R China Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243032 Anhui Peoples R China

Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify parameters of different PV models, this paper proposes an improved jaya algorithm based on self-adaptive method, termed Sjaya. Sjaya incorporates three position update strategies, all utilizing adaptive factors, automatically transitioning from explorative to exploitative behaviors, enhancing the population's ability to escape local optima in the solution space and avoiding premature convergence. The first strategy involves learning towards the best and worst individuals in the population, with the individual iteration direction perturbed by adaptive and normal distribution probability factors to enhance population exploration. The second strategy entails learning towards superior and inferior subgroups, effectively leveraging information from the population, with the ranges of these two subgroups continuously evolving throughout the iteration process. In the third strategy, a novel individual selection mechanism is devised, allocating selection probabilities to individuals based on the exploration phase. Individual updates entail learning from three selected individuals within the population, thereby enhancing population diversity. The proposed Sjaya method is employed to address the parameters identification problem of single diode, double diode, and photovoltaic module models of various photovoltaic types. In numerical experiments, each algorithm was tested 30 times. The average root mean square error (RMSE) of Sjaya for the single diode model and double diode model of RTC France were 9.86022E-04 and 9.849674E-04, respectively. In addition, we use three PV modules to detect Sjaya and competing algorithms. The RMSE of Sjaya on the Photo Watt-PWP 201 module, STM6-40/36 module and STP6-120/36 module is 2.431177E-03, 1.772275E

关键词： Parameters identification Photovoltaic cell and module jaya algorithm Optimization

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Enhanced jaya algorithm for Quality-of-Service- Aware Service Composition in the Internet of Things

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 748-755页

作者： Shi, Yan Hebei Chem & Pharmaceut Coll Shijiazhuang 050026 Peoples R China

The Internet of Things (IoT) has shifted how devices and services interact, resulting in diverse innovations ranging from health and smart cities to industrial automation. Nevertheless, at its core, IoT continues to face one of the major tough tasks of Quality of Service-aware Service Composition (QoS-SC), as these IoT settings are normally transient and unpredictable. This paper proposes an improved jaya algorithm for QoS-SC and focuses on optimizing service selection with a balance between the main QoS attributes: execution time, cost, reliability, and scalability. The proposed approach was designed with adaptive mechanisms to avoid local optima stagnation and slow convergence and thus assure robust exploration and exploitation of the solution area. Incorporating these enhancements, the proposed algorithm outperforms prior metaheuristic approaches regarding QoS satisfaction and computational efficiency. Extensive experiments conducted over diverse IoT scenarios show the algorithm's scalability, demonstrating that it can achieve faster convergence with superior QoS optimization.

关键词： Service composition internet of things quality of service jaya algorithm optimization

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Sustainable 4D TSP to reduce carbon emission using reference point MOQO jaya algorithm in uncertain environment

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JOURNAL OF CLEANER PRODUCTION 2025年 486卷

作者： Bajaj, Aaishwarya S. Dhodiya, Jayesh M. S V Natl Inst Technol Dept Math Surat 395007 Gujarat India

Carbon emissions vary significantly depending on the vehicle type, selected travel route, kerb weight, vehicle speed, and other uncertain factors. Choosing optimal routes and vehicles is crucial for reducing carbon emissions and promoting eco-friendly transportation. This study addresses the sustainable four-dimensional Travelling Salesman Problem (4DTSP) using the proposed Reference Point Aspiration Level-based Multi- Objective Quasi-Oppositional jaya (RPAL-based MOQO jaya) algorithm. The 4DTSP incorporates multiple routes and vehicles for travel between city nodes, addressing uncertainties in travel cost, time, risk, and carbon emissions. Two models, Expected Value (EV) and Optimistic Value (OV), are employed under uncertain conditions, with risk and carbon emissions considered as constraints in the sustainable 4DTSP. A real-world case study of Surat City is conducted for 10 and 50 city nodes to analyse the impact of route and vehicle choices. Road conditions playa critical role in determining vehicle selection, route, and speed. The proposed method's effectiveness is demonstrated through comparisons with existing approaches, including AL-based MOQO jaya, Fuzzy Programming Technique (FPT), Hybrid GA, and Rao algorithms, applied to TSPLIB and the Surat City example. Additionally, sensitivity analysis is performed with respect to confidence levels. The versatility of the proposed algorithm extends to applications in supply chain optimization, ship routing, vehicle routing problems (VRP), and more.

关键词： Four-dimensional travelling salesman problem (4DTSP) Carbon emission Reference point Uncertain environment jaya algorithm

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Dual-Stage jaya algorithm for optimizing valve openings in heating system networks

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Xu, Chao Jia, Yangli Sang, Hongyan Meng, Leilei Zhang, Biao Zou, Wenqiang Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China

With the improvement of people's living standards, more and more industries are focusing on enhancing user satisfaction. However, research on optimizing the balancing valve openings in secondary heating networks to improve user satisfaction is relatively scarce. To address this gap, this paper aims to maximize user satisfaction by solving the balancing valve opening adjustment (BVOA) problem. For this problem, a mixed-integer linear programming model is established, and the dual-stage jaya (Djaya) algorithm is proposed. In Djaya, a problembased random initialization method is introduced to generate high-quality solutions, followed by the development of a dynamic sculpting stage to enhance the global search capability of the algorithm. Additionally, an enhance core stage is designed to optimize the low-quality parts of the solution, thereby improving the overall quality of the solution. Finally, extensive experiments with different heating scales and numbers of balancing valves demonstrate the effectiveness of Djaya in solving the BVOA problem. Adjusting the balancing valves based on the solutions obtained by the Djaya algorithm can, on average, improve user satisfaction by approximately 7.89%.

关键词： jaya algorithm Balancing valve Opening adjusting Satisfaction Secondary network

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Sustainable optimization of balancing valve settings in urban heating systems with an enhanced jaya algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 261卷

作者： Zou, Wenqiang Xu, Chao Jia, Yangli Meng, Leilei Zhang, Biao Sang, Hongyan Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China

With the continuous growth of global energy demand, energy conservation in heating has become a research hotspot for urban sustainable development. However, the research on energy conservation by adjusting the balance valve in the secondary network of heating system is relatively few. To address this gap, this paper solves the balancing valve opening adjustment (BVOA) problem with the goal of minimizing heat consumption and the energy usage of circulation pumps. A mixed-integer linear programming model is designed for this problem, and an effective Ejaya algorithm is proposed. Initially, a random initialization method with three scrambling strategies is employed to generate high-quality solutions. Moreover, a segmented operator is proposed to enhance the algorithm's search efficiency. Subsequently, Q-learning is utilized to select from three neighborhood operators, enhancing the algorithm's local search capabilities. Additionally, an adaptive search balance strategy is designed to balance the methods of updating solutions, aiding in finding better solutions. Finally, extensive experiments have validated the effectiveness and efficiency of the proposed algorithm in solving the BVOA problem, contributing to the promotion of urban energy sustainability and social welfare.

关键词： jaya algorithm Balancing valve Optimization Energy consumption Heating system

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