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A Bi-Population Competition Adaptive interior search algorithm Based on Reinforcement Learning for Flexible Job Shop Scheduling Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2025年第1期24卷

作者： Jiang, Tianhua Liu, Lu Ludong Univ Sch Transportat Yantai 264025 Shandong Peoples R China

In this paper, a bi-population competition adaptive interior search algorithm (BCAISA) based on a reinforcement learning strategy is proposed for the classical flexible job shop scheduling problem (FJSP) to optimize the makespan. First, the scheduling solution is represented using a machine-job-based two-segment integer encoding method, and various heuristic rules are then applied to generate the initial population. Secondly, a bi-population mechanism is introduced to partition the population into two distinct sub-populations. These sub-populations are specifically tailored for machine assignment and operation permutation, employing different search strategies respectively, aiming to facilitate an efficient implementation of parallel search. A competition mechanism is introduced to facilitate the information exchange between the two sub-populations. Thirdly, the ISA is adapted for the discrete scheduling problem by discretizing a series of search operators, which include composition optimization, mirror search, and random walk. A Q-learning-based approach is proposed to dynamically adjust a key parameter, aiming to strike a balance between the capacity for global exploration and local exploitation. Finally, extensive experiments are conducted based on 10 well-known benchmark instances of the FJSP. The design of the experiment (DOE) method is employed to determine the algorithm's parameters. Based on the computational results, the effectiveness of four improvement strategies is first validated. The BCAISA is then compared with fifteen published algorithms. The comparative data demonstrate that our algorithm outperforms other algorithms in 50% of benchmark instances. Additionally, according to the relative percentage deviation (RPD) from the state-of-the-art results, the BCAISA also exhibits superior performance. This highlights the effectiveness of our algorithm for solving the classical FJSP. To enhance the practical application, the scope of the ISA will be broaden

关键词： Flexible job shop scheduling makespan bi-population mechanism competition mechanism interior search algorithm Q-learning strategy

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interior search algorithm (ISA): A novel approach for global optimization

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ISA TRANSACTIONS 2014年第4期53卷 1168-1183页

作者： Gandomi, Amir H. Univ Akron Akron OH 44325 USA

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： interior search algorithm Global optimization Metaheuristic

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Multiobjective optimal power flow using interior search algorithm: A case study on a real-time electrical network

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COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 1078-1096页

作者： Chandrasekaran, Shilaja Kalasalingam Acad Res & Educ Krishnankoil Tamil Nadu India

Optimal power flow (OPF) is a vital concern in an electrical network. In consequence of the intricacy of the power systems, the conventional formulations are not adequate for current situation. Hence, in this study, the multiobjective OPF (MOOPF) problem has been modeled to diminish the production cost, environmental emission, and losses and to enhance the voltage stability and voltage profile simultaneously. This study proposes the application of interior search algorithm (ISA) for resolving MOOPF problem. The simulations have been carried out on three various test systems such as IEEE 30-bus system, IEEE 57-bus system, and Tamil Nadu Generation and Distribution Corporation Limited, as a real part of 62 bus Indian utility system (IUS) to infer the efficacy of ISA in solving the OPF problems. The simulation results have been compared with other techniques. The comparison shows that ISA is used in resolving MOOPF problems.

关键词： interior search algorithm losses optimal power flow pollutant emission production cost voltage deviation and Tamil Nadu power system

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Large Scale Reservoirs System Operation Optimization: the interior search algorithm (ISA) Approach

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WATER RESOURCES MANAGEMENT 2016年第10期30卷 3389-3407页

作者： Moravej, Mojtaba Hosseini-Moghari, Seyed-Mohammad Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Karaj Iran

Reservoirs are built to provide a powerful tool to control and manage surface water resources in order to cover inconsistency between water resources and demands. Due to finite available water and the increasing demands for water especially in arid and semi-arid regions like Iran, reservoirs must be optimally operated in order to use water in the most efficient way. This study applies the interior search algorithm (ISA) to solve large scale reservoirs system operation optimization problems. The ISA is a meta-heuristic algorithm inspired from a systematic methodology of architecture process and mirror work utilized by Persian designers for decoration. Unlike other meta-heuristic algorithms, the ISA just have one parameter to tune which is a great advantage. In this study the parameter of the ISA tuned automatically using a linear equation. A real-world one-reservoir operation problem (i.e. Karun-4) and two large scale benchmark problems (i.e. four-reservoir and ten-reservoir operation problem) were employed to show the effectiveness of the ISA. The results shows the high ability of the ISA to solve reservoirs system operation problems as it achieved solutions 99.97, 99.99 and 99.95 % of global optimum for Karun-4 reservoir, four-reservoir and ten-reservoir system operation problems, respectively. These results are the best results reported so far in the studied problems. Comparing results of the ISA with those of non-linear programming (NLP), linear programming (LP), genetic algorithm (GA) and other meta-heuristic algorithms indicates fast convergence to global optimum. Considering the results, it can be stated that the ISA is a powerful tool to optimize complex large scale reservoir system operation problems.

关键词： interior search algorithm Reservoir operation Karun-4 reservoir Optimization Four-reservoir system Ten-reservoir system

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An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2173-2189页

作者： Trivedi, Indrajit N. Jangir, Pradeep Bhoye, Motilal Jangir, Narottam Govt Engn Coll Dept Elect Engn Gandhinagar 389001 Gujarat India Lukhdhirji Engn Coll Dept Elect Engn Morbi 363641 Gujarat India

Microgrid is a novel small-scale system of the centralized electricity for a small-scale community such as villages and commercial area. Microgrid consists of micro-sources like distribution generator, solar and wind units. A microgrid is consummate specific purposes like reliability, cost reduction, emission reduction, efficiency improvement, use of renewable sources and continuous energy source. In the microgrid, the Energy Management System is having a problem of Economic Load Dispatch (ELD) and Combined Economic Emission Dispatch (CEED) and it is optimized by meta-heuristic techniques. The key objective of this paper is to solve the Combined Economic Emission Dispatch (CEED) problem to obtain optimal system cost. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The newly introduced interior search algorithm (ISA) is applied for the solution of ELD and CEED problem. The minimization of total cost and total emission is obtained for four different scenarios like all sources included all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of ISA with the Reduced Gradient Method (RGM), Ant Colony Optimization (ACO) technique and Cuckoo search algorithm (CSA) for the two different cases which are ELD without emission and CEED with emission. The results are calculated for different Power Demand of 24h. The results obtained to ISA give comparatively better cost reduction as compared with RGM, ACO and CSA which shows the effectiveness of the given algorithm.

关键词： Microgrid Economic load dispatch Emission dispatch Wind generation prediction Solar generation prediction interior search algorithm

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Retaining wall optimization using interior search algorithm with different bound constraint handling

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INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS 2017年第11期41卷 1304-1331页

作者： Gandomi, A. H. Kashani, A. R. Zeighami, F. Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA Arak Univ Dept Civil Engn Arak Iran Univ Bologna Sch Engn & Architecture DICAM Dept Bologna Italy

Along with the applicability of optimization algorithms, there are lots of features that can affect the functioning of the optimization techniques. The main purpose of this paper is investigating the significance of boundary constraint handling (BCH) schemes on the performance of optimization algorithms. To this end, numbers of deterministic and probabilistic BCH approaches are applied to one of the most recent proposed optimization techniques, named interior search algorithm (ISA). Apart from the implementing different BCH methods, a sensitivity analysis is conducted to find an appropriate setting for the only parameter of ISA. Concrete cantilever retaining wall design as one of the most important geotechnical problems is tackled to declare proficiency of the ISA algorithm, on the one hand, and benchmark the effect of BCH schemes on the final results, on the contrary. As results demonstrate, various BCH approaches have a perceptible impact on the algorithm performance. In like manner, the essential parameter of ISA can also play a pivotal role in this algorithm's efficiency. Copyright (c) 2017 John Wiley & Sons, Ltd.

关键词： interior search algorithm retaining wall bound constraint handling metaheuristic algorithms

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Multi-objective economic emission dispatch using interior search algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第1期29卷 e2683.1-e2683.18页

作者： Karthik, Nagarajan Parvathy, Ayalur Krishnamoorthy Arul, Rajagopalan Hindustan Inst Technol & Sci Dept Elect & Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai Campus Chennai Tamil Nadu India

The purpose of the combined economic emission dispatch of generation in an electric power is to offer the finest schedule for the generating units which must run with both lesser fuel cost and emission levels concurrently thereby fulfilling the system equality and inequality constraints. This paper presents an interior search algorithm (ISA) for solving multi-objective combined economic emission dispatch (CEED) problems. Simulation results obtained substantiate the efficiency of ISA algorithm in solving the CEED problems. The effectiveness of the proposed ISA is investigated on five various test systems which comprise three-unit system, IEEE 30-bus system, 10-unit system, 20-unit system, and Taiwan 40-unit generating system. Simulation results and their comparison with other meta-heuristic optimization techniques reveal that the proposed ISA is proficient in solving CEED problem of fossil fuel generators.

关键词： combined economic emission dispatch interior search algorithm meta-heuristic algorithm valve point loading effect

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An interior search algorithm based on chaotic and crossover strategies for parameter extraction of polyphase induction machines

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6647-6664页

作者： Rizk-Allah, Rizk M. Abdelwanis, Mohamed, I El-Sehiemy, Ragab A. Abd-Elrazek, Ahmed S. Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafrelsheikh Egypt Kafrelsheikh Univ Fac Engn Dept Basic Engn Sci Kafrelsheikh Egypt

The accuracy of the extracted parameters is important for studying the polyphase induction motor performance and/or the motor control schemes. An investigated and improved interior search algorithm (IISA) is presented in this study for extracting the optimal values of estimated parameters of six-phase and three-phase induction motors. This investigation was carried out on two polyphase induction motors as experimental research cases, utilizing features of manufacturer's operation. The estimated parameters show the high capability regarding the performance of the desired IISA optimizer. The performance of the proposed IISA is compared with different modern optimization algorithms including the basic ISA, and other state-of-the-art approaches. Experimental verifications are validated on two polyphase induction motors, called six-phase and three-phase induction motors. The obtained results show that the proposed method is very competitive in extracting the unknown parameters of different induction motor models with a high degree of closeness to the experimental records. Moreover, various statistical tests, such as the Wilcoxon rank test, stability analysis, and convergence analysis, have been conducted to justify the performance of the proposed IISA. From all the analyses, it has been revealed that the proposed IISA is a competitive method compared to other popular state-of-the-art competitors and ISA variant with accurately identified parameters.

关键词： interior search algorithm Statistical analysis Parameter estimation Robustness Induction motors Experimental records

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A self-learning interior search algorithm based on reinforcement learning for energy-aware job shop scheduling problem with outsourcing option

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10085-10100页

作者： Liu, Xinyu Liu, Lu Jiang, Tianhua Yantai Automobile Engn Profess Coll Sch Vehicle Applicat Engn Yantai 265599 Peoples R China Ludong Univ Sch Transportat Yantai Shandong Peoples R China

Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem.

关键词： Job shop outsourcing option energy-aware scheduling interior search algorithm

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Discrete interior search algorithm for Multi-resource Fair Allocation in Heterogeneous Cloud Computing Systems 12th

Discrete Interior Search Algorithm for Multi-resource Fair A...

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12th International Conference on Intelligent Computing (ICIC)

作者： Liu, Xi Zhang, Xiaolu Li, Weidong Zhang, Xuejie Yunnan Univ Sch Informat Sci & Engn 2 North Cuihu Rd Kunmming 650091 Yunnan Peoples R China

ISBN: (纸本)9783319422916;9783319422909

The mechanism of resource allocation for cloud computing not only affects the users' fairness, but also has a significant impact on resource utilization. Most current resource allocation models did not take into account the indivisible demands, the heterogeneity servers, and the situations multi-server. Dominant resource fairness allocation in heterogeneous systems (DRFH) is a fair and efficient resource allocation mechanism. But solving the DRFH problem is NP-hard. There are significant gaps between solutions obtained by existing heuristic algorithms and optimal solutions. They cannot effectively use server resources, resulting in a waste of resources of servers. In this paper, we propose a novel discrete interior search algorithm (DISA) to solve indivisible demands in heterogeneous servers, with a specific repair operator and task-fit value. Experimental results demonstrate that DISA can well adapt to dynamic changes in user resource request type, obtain the near-optimal solutions, maximize the value of minimum global dominant share and resource utilization.

关键词： Dominant resource fairness interior search algorithm Multi-resource fair allocation Heterogeneous cloud

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