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A discrete jaya algorithm for vehicle routing problems with uncertain demands

SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Zhang, Jing Ye, Jing-Xuan Lin, Jian Song, Hong-Bo Zhejiang Police Coll Dept Comp & Informat Secur Hangzhou Peoples R China Zhejiang Univ Finance & Econ Sch Informat Technol & Artificial Intelligence Hangzhou Peoples R China Zhejiang Shuren Univ Coll Informat Sci & Technol Hangzhou Peoples R China

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete jaya (Djaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in Djaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed Djaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

关键词： jaya algorithm vehicle routing problem uncertain demand local search

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A decomposition-based multi-objective jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Xie, Fawu Li, Lingling Li, Li Huang, Yangpeng He, Zaixiang Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in sol

关键词： Lot streaming Job shop scheduling Variable sublots Intermingling Decomposition jaya algorithm

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An Efficient jaya algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems 55

An Efficient Jaya Algorithm for Joint Optimization of Preven...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Mishra, Aseem K. Shrivastava, Divya Rastogi, Rajat Department of Operations and Decision Sciences Birla Institute of Management Technology Knowledge Park-II U.P. Greater Noida201306 India Department of Mechanical Engineering Shiv Nadar University NH-91 Tehsil Dadri U.P. 201314 India

The present paper develops an integrated approach for joint optimization of preventive maintenance (PM) planning and quality control policy in a single machine manufacturing system. Considering the economic dependency between quality and maintenance parameters, the proposed model aims to optimize the four decision variables, viz. PM interval, sample size, sample frequency and control limit coefficient to minimize the expected total integrated cost per unit time. The imperfect age-based PM model is applied which addresses the most practical issue that a unit may not always be maintained to an as-good-as-new state. Analysing the problem to be NP-hard in nature, a recently developed meta-heuristic named jaya algorithm is applied to retrieve the near-optimum solutions. The peculiarity of jaya as compared to conventional heuristics is that it is a parameter-less algorithm thus does not require tuning to algorithm-specific parameters thereby minimizing computational complexity to a great extent. A numerical example is presented to demonstrate the effectiveness of the integrated economic design of PM and process quality control parameters. Computational results reveal the effectiveness of the proposed approach. © 2022 The Authors. Published by Elsevier B.V.

关键词： Integration jaya algorithm Preventive Maintenance Quality Control

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jayaL: A Novel jaya algorithm Based on Elite Local Search for Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第9期46卷 8925-8952页

作者： Tefek, Mehmet Fatih Beskirli, Mehmet Osmaniye Korkut Ata Univ Dept Comp Engn Osmaniye Turkey Sirnak Univ Dept Comp Engn Sirnak Turkey

Many metaheuristic methods have been proposed to solve engineering problems in literature studies. One of these is the jaya algorithm, a new population-based optimization algorithm that has been suggested in recent years to solve complex and continuous optimization problems. jaya basically adopts the best solution by avoiding the worst ones. Although this process accelerates the convergence for the solution, it causes concessions in the population and results in inadequate local search capacity. To increase the search capability and exploitation performance of the jaya algorithm, a new local search procedure-Elite Local Search-has been added to the jaya algorithm in this study without making any changes in its basic search capability. Thus, an efficient and robust strategy that can overcome continuous optimization problems is presented. This new algorithm created with the elite local search procedure is called jayaL. To demonstrate the performance and accuracy of jayaL, 20 different well-known benchmark functions in the literature were used. In addition to jayaL algorithm, these functions were solved with differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), atom search optimization (ASO) and jaya algorithms. The performances of jayaL, DE, PSO, ABC DA, GOA, ASO and jaya algorithms were compared with each other, and experimental results were supported by convergence graphs. At the same time, jayaL has been applied to constrained real-world engineering problems. According to the analyses, it has been concluded that jayaL algorithm is a robust and efficient method for continuous optimization problems.

关键词： jaya algorithm Elite local search Benchmark functions Optimization

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An Improved Discrete jaya algorithm for Shortest Path Problems in Transportation-Related Processes

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PROCESSES 2023年第8期11卷 2447页

作者： Wang, Ren Zhou, Mengchu Wang, Jinglin Gao, Kaizhou Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Shortest path problems are encountered in many engineering applications, e.g., intelligent transportation, robot path planning, and smart logistics. The environmental changes as sensed and transmitted via the Internet of Things make the shortest path change frequently, thus posing ever-increasing difficulty for traditional methods to meet the real-time requirements of many applications. Therefore, developing more efficient solutions has become particularly important. This paper presents an improved discrete jaya algorithm (IDjaya) to solve the shortest path problem. A local search operation is applied to expand the scope of solution exploration and improve solution quality. The time complexity of IDjaya is analyzed. Experiments are carried out on seven real road networks and dense graphs in transportation-related processes. IDjaya is compared with the Dijkstra and ant colony optimization (ACO) algorithms. The results verify the superiority of the IDjaya over its peers. It can thus be well utilized to meet real-time application requirements.

关键词： shortest path problem (SPP) jaya algorithm route planning

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jaya algorithm-based energy management system for battery- and ultracapacitor-powered ultralight electric vehicle

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第6期44卷 4977-4985页

作者： Demircali, Akif Koroglu, Selim Pamukkale Univ Dept Elect Elect Engn Denizli Turkey

To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real-time applications. The simulation results show that jaya-EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO-EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real-time applications.

关键词： battery electric vehicle energy management strategy jaya algorithm ultracapacitor

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A comprehensive review on jaya optimization algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第5期56卷 4329-4361页

作者： Aquino da Silva, Luiza Scapinello Souza Lucio, Yan Lieven Coelho, Leandro dos Santos Mariani, Viviana Cocco Rao, Ravipudi Venkata Fed Univ Parana UFPR Elect Engn Grad Program PPGEE BR-81530000 Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Syst Engn Grad Program PPGEPS BR-80215901 Curitiba Parana Brazil Pontifical Catholic Univ Parana PUCPR Mech Engn Grad Program PPGEM BR-80215901 Curitiba Parana Brazil SV Natl Inst Technol Dept Mech Engn Surat 395007 India

The jaya algorithm is a relatively new population-based optimization, which has become a progressively valuable tool in swarm intelligence. The jaya algorithm incorporates the survival of the fittest principle alike evolutionary algorithm by its victorious nature as well as the ideal of an inducement towards a global optimal, which represents its swarm intelligence nature. Nevertheless, it has been applied in various areas of optimization, mainly in engineering practice, which is discussed and abridged based on each problem's *** jaya optimization's vast applicability can be explained by its ability to work without any algorithm-specific parameters. The successfully solved problems may also use some of this meta-heuristic's variants, in which the algorithm has been modified or hybridized. This paper focuses on a comprehensive review, as well as a bibliometric study of the jaya algorithm, to imply its versatility. Hence, this study is likely to emphasize this optimization's abilities, inspiring new researchers to make use of this simple and efficient algorithm for problem-solving.

关键词： Population-based algorithm jaya algorithm Optimization Bibliometric study Survey

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Perturbation based privacy preservation and classification using jaya algorithm and Dragonfly Inspired algorithm

Franklin Open

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Franklin Open 2025年 11卷

作者： Sen, Dipanwita Mishra, Bhupati Bhusan Pattnaik, Prasant Kumar KIIT Deemed To Be University Odisha Bhubaneswar 751024 India

Healthcare datasets are very sensitive datasets. In case of unauthorized access, sensitive datasets could potentially cause damage, discrimination and unsolicited scrutiny. The patients’ health details constitute private personal information. They should not be disclosed. However, data might get stored in cloud without any protection. That is why, privacy preservation of data for healthcare dataset is a significant consideration. In this work, the Wisconsin Prognostic Breast Cancer (WBC) dataset has been used. At first, a privacy preserving schema making use of perturbation implementing jaya algorithm has been elucidated. Out of 30 numerical attributes in the dataset, 6 are chosen for perturbation based on their relatively high Pearson's Correlation coefficient values. They form the initial population of jaya algorithm. The objective function is defined and we opt for a minimization problem. After each iteration, the algorithm generates a new population from the previous population. Thereafter, the accuracies obtained by a few traditional classification algorithms as well as classifiers based on some meta-heuristic algorithms, are observed . The classical classifiers used are Decision Tree, Random Forest, AdaBoost, KNN and GNB. The standard evaluation metrics are recorded thereafter. For privacy, the evaluation metrics used are Secrecy, Value Difference(VD), RP, RK, CP and CK. For utility, the metrics are Accuracy, Precision, Recall, F1-Score and Area Under the Curve(AUC). jaya algorithm is then compared with traditional perturbation algorithms like 2DRT and 3DRT. It is seen that jaya preserves more privacy and retains more utility as suggested by mean Friedman Test Rankings. Among the metaheuristic optimization based classifiers, only the Dragonfly inspired Classifier(DIC) classifies over 90 % of the records correctly for the perturbed dataset. For classification, the perturbed dataset is fed into the DIC as the original population. The target is to minimize the di

关键词： 2DRT 3DRT CK CP Data mining (DM) Dragonfly Independent component analysis (ICA) jaya algorithm Meta-heuristic optimization Particle Swarm Optimization(PSO) Perturbation Privacy preserving data mining (PPDM) RK RP Secrecy VD

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Multi-objective feature selection based on quasi-oppositional based jaya algorithm for microarray data

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107804-107804页

作者： Chaudhuri, Abhilasha Sahu, Tirath Prasad Natl Inst Technol Raipur Dept Informat Technol Chhattisgarh 492010 India

Feature selection is the highly sought-after pre-processing technique for microarray data classification. Microarray data is a phenomenally imbalanced, high-dimensional dataset which requires a specifically designed feature selection technique. A hybrid feature selection (FS) technique is designed in this work for microarray datasets. The proposed FS technique has two phases. In the first phase, aggregate ranking of five well-known filter methods is obtained using the PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations) method and is used to remove the unwanted features. The features selected in the first phase are then passed on to the second phase for further refinement. In the second phase, the quasi-oppositional-based multi-objective jaya algorithm is designed for wrapper-based feature selection. Moreover, a novel quasi-opposite number generation technique for binary numbers is proposed. Ten benchmark microarray datasets are used to evaluate the performance of the proposed technique. Seven other state-of-the-art feature selection techniques are used for comparison and the proposed technique is found to outperform other techniques in terms of classification error rate and the selected number of features. In addition, the proposed FS technique is found to be ten times faster than other FS techniques. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-objective feature selection Microarray data jaya algorithm Quasi-oppositional PROMETHEE

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Image and video dehazing based on transmission estimation and refinement using jaya algorithm

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OPTIK 2022年 265卷

作者： Ashwini, K. Nenavath, Hathiram Jatoth, Ravi Kumar Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 Andhra Pradesh India Vardhaman Coll Engn Autonomous Dept Elect & Commun Engn Hyderabad 501218 India

Due to its in-built ill-posed nature, image and video dehazing is an immensely thought-provoking problem, and it is an essential pre-processing step for various applications such as ADAS (automatic driver-assistance systems), security and surveillance, remote sensing, and long-range imaging. We proposed a novel single image and video dehazing method, called JA-dehaze, by using jaya algorithm (JA) to address this problem. The technique consists of three factors: Discrete Haar Wavelet Transform (DHWT) for sub-bands, local atmospheric light estimation and transmission map estimation and refinement using jaya algorithm. We formulated haze removal as an optimization problem, and the fitness function for the single image is taken from the information loss term, distance and the haze density term. The JA-dehaze method improves the gradient and preserves the information optimally by minimizing the fitness function. Furthermore, we extended the single image dehazing process to real-time video dehazing. Experimental results demonstrate that the proposed JA-dehaze method can successfully improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.

关键词： Video dehazing jaya algorithm JA-dehaze Transmission estimation Optimization algorithm

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