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hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions

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Natural Gas Industry B 2025年第2期12卷 234-250页

作者： Zhou, Jun Li, Zichen Liu, Shitao Li, Chengyu Zhao, Yunxiang Zhou, Zonghang Liang, Guangchuan Petroleum Engineering School Southwest Petroleum University Chengdu China Yunnan Provincial Energy Research Institute Co. Ltd. Kunming China

The surface injection and production system (SIPS) is a critical component for effective injection and production processes in underground natural gas storage. As a vital channel, the rational design of the surface injection and production (SIP) pipeline significantly impacts efficiency. This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects. An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model. This paper proposes a hybrid genetic algorithm generalized reduced gradient (HGA-GRG) method, and compares it with the traditional genetic algorithm (GA) in a practical case study. The HGA-GRG demonstrated significant advantages in optimization outcomes, reducing the initial cost by 345.371 × 10⁴ CNY compared to the GA, validating the effectiveness of the model. By adjusting algorithm parameters, the optimal iterative results of the HGA-GRG were obtained, providing new research insights for the optimal design of a SIPS. © 2025 Sichuan Petroleum Administration

关键词： hybrid genetic algorithm Parameter optimization Surface injection and production pipeline Underground natural gas storage

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An effective hybrid genetic algorithm for the multi-robot task allocation problem with limited span

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Liu, Wenbo Kuang, Zhian Zhang, Yongcong Zhou, Bo He, Pengfei Li, Shihua Southeast Univ Sch Automat Nanjing Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China Shanghai Friendess Elect Technol Shanghai 201100 Peoples R China

Multi-robot task allocation is one of the most interesting multi-robot systems that have gained considerable attention due to various real-world applications. In this paper, we focus on a multi-robot task allocation problem where a set of industrial robots, which are installed on a gantry and have a limited working span, have to jointly perform a set of weld lines in large workpieces. Considering the emphasis on minimizing the processing time of workpieces in industry, the objective of this problem is to minimize the cycle time when scheduling a set of robots to work together efficiently. Following practical applications, we present a mathematical model for small size instances, and for large size instances, we propose an effective hybrid genetic algorithm to solve it because of the significant computational complexity, which includes a specific region division method is used to divide the workpieces into a set of regions where the robots can reach all the weld lines in each region, a dedicated route-based crossover to generate promising offspring solutions, and an effective neighborhood-based local search procedure to improve each offspring solution as much as possible. Extensive experimental results on three benchmark instances show that the algorithm significantly outperforms two refer methods with an average improvement of 6.06% and 4.6%. Additional experiments on real-world instances are presented to verify the algorithm's effectiveness in solving the multi-robot task allocation problem with limited span.

关键词： Task allocation Limited span hybrid genetic algorithm Weld line Multi-robot

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The dual-resource-constrained re-entrant flexible flow shop a constraint programming approach and a hybrid genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第5期63卷 1803-1824页

作者： Mlekusch, Johanna Hartl, Richard F. Univ Vienna Dept Business Decis & Analyt A-1010 Vienna Austria

The dual-resource-constrained re-entrant flexible flow shop scheduling problem represents a specialised variant of the flow shop scheduling problem, inspired by real-world scenarios in screen printing industries. Besides the well-known flow shop structure, stages consist of identical parallel machines and operations may re-enter the same stage multiple times before completion. Moreover, each machine must be operated by a skilled worker, making it a dual-resource-constrained problem according to the existing literature. The objective is to minimise the total length of the production schedule. To address this problem, our study employs two methods: a constraint programming model and a hybrid genetic algorithm with a single-level solution representation and an efficient decoding heuristic. To evaluate the performance of our methods, we conducted a computational study using different problem instances. Our findings demonstrate that the proposed hybrid genetic algorithm consistently delivers high-quality solutions, particularly for large instances, while also maintaining a short computational time. Additionally, our methods improve existing benchmark results for instances from the literature for a subclass of the problem. Furthermore, we provide managerial insights into how dual-resource constraints affect the solution quality and the efficiency associated with different workforce configurations in the described production setting.

关键词： Production flexible re-entrant flow shop scheduling dual-resource hybrid genetic algorithm constraint programming

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hybrid genetic algorithm with Wiener process for multi-scale colored balanced traveling salesman problem

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 262卷

作者： Dong, Xueshi Ma, Liwen Zhao, Xin Shan, Yongchang Wang, Jie Xu, Zhenghao Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Shandong Univ Shandong Prov Key Lab Software Engn Jinan 250101 Peoples R China Qingdao Univ Sch Math & Stat Qingdao 266071 Peoples R China

Colored traveling salesman problem (CTSP) can be applied to Multi-machine Engineering Systems (MES) in industry, colored balanced traveling salesman problem (CBTSP) is a variant of CTSP, which can be used to model the optimization problems with partially overlapped workspace such as the planning optimization (For example, process planning, assembly planning, productions scheduling). The traditional algorithms have been used to solve CBTSP, however, they are limited both in solution quality and solving speed, and the scale of CBTSP is also restricted. Moreover, the traditional algorithms still have the problems such as lacking theoretical support of mathematical physics. In order to improve these, this paper proposes a novel hybrid genetic algorithm (NHGA) based on Wiener process (ITO process) and generating neighborhood solution (GNS) to solve multi-scale CBTSP problem. NHGA firstly uses dual-chromosome coding to construct the solutions of CBTSP, then they are updated by the crossover operator, mutation operator and GNS. The crossover length of the crossover operator and the city number of the mutation operator are controlled by activity intensity based on ITO process, while the city keeping probability of GNS can be learned or obtained by Wiener process. The experiments show that NHGA can demonstrate an improvement over the state-of-art algorithms for multi-scale CBTSP in term of solution quality.

关键词： hybrid genetic algorithm Wiener process Generating neighborhood solution Multi-scale optimization Colored balanced traveling salesman problem

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A hybrid genetic algorithm for a type of nonlinear programming problem

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 1998年第5期36卷 11-21页

作者： Tang, JF Wang, DW Ip, A Fung, RYK Northeastern Univ Sch Informat Sci & Engn Shenyang 110006 Peoples R China Hong Kong Polytech Univ Dept Mfg Engn Kowloon Peoples R China City Univ Hong Kong Dept Mfg Engn & Engn Management Kowloon Peoples R China

Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD1) of semifeasible direction, feasible degree (FD2) of illegal points 'belonging to' feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special hybrid genetic algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： nonlinear programming hybrid genetic algorithm weighted gradient direction feasible degree semifeasible direction

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Sequencing by hybridization: an enhanced crossover operator for a hybrid genetic algorithm

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JOURNAL OF HEURISTICS 2007年第3期13卷 209-225页

作者： Brizuela, Carlos A. Gonzalez-Gurrola, Luis C. Tchernykh, Andrei Trystram, Denis CICESE Res Ctr Dept Comp Sci Ensenada Baja California Mexico Inst Tecnol Super Santiago Papasquiaro Santiago Papasquiaro 34600 Dgo Mexico Inst IMAG ID F-38041 Grenoble France

This paper presents a genetic algorithm for an important computational biology problem. The problem appears in the computational part of a new proposal for DNA sequencing denominated sequencing by hybridization. The general usage of this method for real sequencing purposes depends mainly on the development of good algorithmic procedures for solving its computational phase. The proposed genetic algorithm is a modified version of a previously proposed hybrid genetic algorithm for the same problem. It is compared with two well suited meta-heuristic approaches reported in the literature: the hybrid genetic algorithm, which is the origin of our proposed variant, and a tabu-scatter search algorithm. Experimental results carried out on real DNA data show the advantages of using the proposed algorithm. Furthermore, statistical tests confirm the superiority of the proposed variant over the state-of-the-art heuristics.

关键词： sequencing by hybridization hybrid genetic algorithm greedy crossover

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hybrid genetic algorithm with adaptive local search scheme

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COMPUTERS & INDUSTRIAL ENGINEERING 2006年第1期51卷 128-141页

作者： Yun, YoungSu Chosun Univ Sch Business Adm Kwangju 501759 South Korea

This paper proposes a hybrid genetic algorithm (a-hGA) with adaptive local search scheme. For designing the a-hGA, a local search technique is incorporated in the loop of genetic algorithm (GA), and whether or not the local search technique is used in the GA is automatically determined by the adaptive local search scheme. Two modes of adaptive local search schemes are developed in this paper. First mode is to use the conditional local search method that can measure the average fitness values obtained from the continuous two generations of the a-hGA, while second one is to apply the similarity coefficient method that can measure a similarity among the individuals of the population of the a-hGA. These two adaptive local search schemes are included in the a-hGA loop, respectively. Therefore, the a-hGA can be divided into two types: ahGA1 and a-hGA2. To prove the efficiency of the a-hGA1 and a-hGA2, a canonical GA (cGA) and a hybrid GA (hGA) with local search technique and without any adaptive local search scheme are also presented. In numerical example, all the algorithms (cGA, hGA, a-hGA1 and a-hGA2) are tested and analyzed. Finally, the efficiency of the proposed a-hGA1 and a-hGA2 is proved by various measures of performance. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： hybrid genetic algorithm adaptive local search scheme adaptive local search technique

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hybrid genetic algorithm and model-free coupled direct search methods for kinetics of nanocrystalline ZSM-5-catalyzed decomposition of PP

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APPLIED CATALYSIS A-GENERAL 2008年第2期351卷 195-203页

作者： Reddy, P. Karthik Chowlu, Aie Cheng King Ghoshal, A. K. Indian Inst Technol Dept Chem Engn Gauhati 39 Assam India

Catalytic decomposition of polypropylene over nanocrystalline HZSM-5 was investigated. The optimum catalyst composition was found to be 50 wt.% where the reduction in maximum decomposition temperature is around 161 degrees C. Kinetics parameters were estimated based on different decomposition models and multi-heating rate experimental data. We employed the hybrid genetic algorithm (HGA) and the model-free coupled direct search (MFCDS) methods to obtain the optimized kinetics triplet values. Both the methods employed in the present study gave almost the same optimized kinetics triplet values. According to Akaike's Information Criteria, the nucleation and growth model with reaction order n = 2/3 and the first-order chemical reaction model were found to be the most suited models. The nucleation and growth model with reaction order It = 2/3 very well predicted the experimental TGA data. The catalyst was fairly active even after its use for the 20th cycle without regeneration. Catalytic decomposition produced more lighter hydrocarbons compared to noncatalytic decomposition. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Catalytic decomposition hybrid genetic algorithm Kinetics parameters Model-free coupled direct search method n-HZSM-5 catalyst Polypropylene Catalyst reusability

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hybrid genetic algorithm for the open capacitated arc routing problem

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COMPUTERS & OPERATIONS RESEARCH 2018年 90卷 221-231页

作者： Arakaki, Rafael Kendy Usberti, Fabio Luiz Univ Estadual Campinas Inst Comp Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

The Open Capacitated Arc Routing Problem (OCARP) is an NP-hard arc routing problem where, given an undirected graph, the objective is to find the least cost set of routes that services all edges with positive demand (required edges). The routes are subjected to capacity constraints in relation to edge demands. The OCARP differs from the Capacitated Arc Routing Problem (CARP) since OCARP does not consider a depot and routes are not constrained to form cycles. A hybrid genetic algorithm with feasibilization and local search procedures is proposed for the OCARP. Computational experiments conducted on a set of benchmark instances reveal that the proposed hybrid genetic algorithm achieved the best upper bounds for almost all instances. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Open capacitated arc routing problem hybrid genetic algorithm Metaheuristic

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hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling

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COMPUTERS IN INDUSTRY 2005年第2期56卷 143-160页

作者： Kim, KW Yun, YS Yoon, JM Gen, M Yamazaki, G Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Tokyo Metropolitan Inst Technol Dept Intelligent Syst Tokyo 1900065 Japan Daegu Univ Sch Automot Ind & Mech Engn Kyungbuk 712714 South Korea Seoul Natl Univ Technol Dept Comp Sci & Engn Seoul 139743 South Korea

In this paper, we propose a hybrid genetic algorithm with fuzzy logic controller (flc-hGA) to solve the resource-constrained multiple project scheduling problem (rc-mPSP) which is well known NP-hard problem. Objectives described in this paper are to minimize total project time and to minimize total tardiness penalty. However, it is difficult to treat the rc-mPSP problems with traditional optimization techniques. The proposed new approach is based on the design of genetic operators with fuzzy logic controller (FLC) through initializing the revised serial method which outperforms the non-preemptive scheduling with precedence and resources constraints. For these rc-mPSP problems, we demonstrate that the proposed flc-hGA yields better results than conventional genetic algorithms and adaptive genetic algorithm. (C) 2004 Elsevier B.V. All rights reserved.

关键词： resource-constrained project scheduling problem scheduling hybrid genetic algorithm fuzzy logic controller

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