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An efficient hybrid evolutionary algorithm for solving the traveling salesman problem

Procedia Computer Science 2024年 246卷 3566-3574页

作者： Piotr Jędrzejowicz Krzysztof Keller Aleksander Skakovski Department of Information Systems Gdynia Maritime University str. Morska 83F Gdynia 81-225 Poland

The paper proposes an effective hybrid ACO-GA evolutionary algorithm combining the best features of the Ant Colony Optimization and Genetic algorithm. The algorithm was tested on benchmark instances of the traveling salesman problem from TSPLIB and compared to several algorithms described in the scientific literature solving the same instances. The comparison results allow us to perceive the proposed ACO-GA as an attractive compromise between the quality of solutions and the time to find a solution.

关键词： hybrid evolutionary algorithm Ant Colony Optimization Genetic algorithm Travelling Salesman Problem

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Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based hybrid evolutionary algorithm

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INTERNET OF THINGS 2024年 27卷

作者： Singh, Shailendra Pratap Singh, Prabhishek Diwakar, Manoj Kumar, Pardeep Technol Bennett Univ Sch Comp Sci Engn Greater Noida India Graph Era Univ Dept CSE Dehra Dun Uttarakhand India Graph Era Hill Univ Dept Math Dehra Dun Uttarakhand India Jaypee Univ Informat Technol Dept Comp Sci & Engn Solan Himachal Prades India

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the "Adaptation-Based hybrid evolutionary algorithm," this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm's control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.

关键词： hybrid evolutionary algorithm Sensors Quality of service Internet of things Smart agriculture

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LOCATING TYPE-1 LOAD FLOW SOLUTIONS USING hybrid evolutionary algorithm

LOCATING TYPE-1 LOAD FLOW SOLUTIONS USING HYBRID EVOLUTIONAR...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： T.O.TING K.P.WONG C.Y.CHUNG Computational Intelligence Applications Research Laboratory (CIARLab) Department of Electrical EngineeringThe Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Conventional methods can only locate some of the Type-1 load flow solutions in power systems and the located solutions are inadequate for voltage stability assessment. This paper proposes a hybrid Genetic algorithm/Particle Swarm Optimization algorithm for locating all the Type-1 load flow solutions. The characteristics of the Type-1 solutions are described in the paper. The hybrid algorithm is applied to two test systems. The test results obtained are satisfactory and promising for voltage stability monitoring applications.

关键词： Bifurcation hybrid evolutionary algorithm Load flow analysis Voltage stability

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Designing foam filled sandwich panels for blast mitigation using a hybrid evolutionary optimization algorithm

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COMPOSITE STRUCTURES 2016年第0期158卷 72-82页

作者： Karen, Idris Yazici, Murat Shukla, Arun Bursa Orhangazi Univ Dept Mech Engn Fac Engn Yildirim Campus TR-16310 Bursa Turkey Uludag Univ Fac Engn Automot Engn Dept TR-16059 Bursa Turkey Univ Rhode Isl Dynam Photomech Lab Dept Mech Ind & Syst Engn 92 Upper Coll Rd Kingston RI 02881 USA

Developing sandwich structures with high energy absorption capability is important for shock loading applications. In the present study, a hybrid evolutionary optimization technique based on Multi-Island Genetic algorithm and Hooke-Jeeves algorithm is used in the design stage of the sandwich structures to obtain effective results. Optimum parameters of cell geometry were investigated using the hybrid optimization algorithm to design foam filled sandwich panels for three main boundary conditions. Shock tube experiments were conducted in order to simulate the shock load effects along with 3D and 2D finite element analysis. Using the experimental results, a simulation-based design optimization approach was prepared and used to develop the designs of new sandwich structures. Promising results were obtained for all three different boundary conditions. In the simply supported case, 21% improvement of shock absorption was achieved by using 57% less volume of foam with respect to the original fully foam filled sandwich panel. In the clamped-clamped case, 16% improvement of shock absorption with 52% less volume was obtained. In the rigid base case study, 6% improvement of shock absorption with 38% less volume usage was achieved. The structures developed in this study will be of use in the defense, automotive and other industries. (C) 2016 Published by Elsevier Ltd.

关键词： Sandwich panel hybrid evolutionary algorithm Blast loading Corrugated steel core Polymer foam infill Shock tube

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Research on hybrid evolutionary algorithms with Differential Evolution and GUO Tao algorithm Based on Orthogonal Design

Research on Hybrid Evolutionary Algorithms with Differential...

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6th International Conference on Intelligent Computing

作者： Zhao, Zhan-Fang Liu, Kun-Qi Li, Xia Zhang, You-Hua Wang, Shu-Lin Shijiazhuang Univ Econ Dept Comp Sci Shijiazhuang 050031 Peoples R China China Univ Geosci Sch Comp Wuhan 430074 Peoples R China Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9783642149214

This paper proposes an orthogonal mutation technology which combines the orthogonal initialization technology and the orthogonal crossover technique They are called the orthogonal processing technologies The fusion of the differential evolution algorithm the GuoTao operator and the orthogonal processing technology has formed several different hybrid evolutionary algorithms The experimental results show that these new algorithms display the good performance in the solution precision the stability and the convergence

关键词： Differential evolution algorithm GuoTao operator orthogonal de sign orthogonal crossover orthogonal mutation hybrid evolutionary algorithm

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Engineering efficiency unleashed: minimum initial marking estimation in labeled Petri nets through a cutting-edge hybrid evolutionary heuristic

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ENGINEERING COMPUTATIONS 2025年第3期42卷 1267-1284页

作者： Yue, Hao Wang, Can Zhao, Zigeng Yang, Xin Wang, Min Hu, Hesuan China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China Shandong Key Lab Intelligent Oil & Gas Ind Softwar Qingdao Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China Xidian Univ Sch Electro Mech Engn Xian Peoples R China Nanyang Technol Univ Coll Engn Sch Comp Sci & Engn Singapore City Singapore

Purpose As widely used by the design and decision-making of resource allocation and communications in manufacturing, monitoring of mobile robot network and other engineering fields, minimum initial marking (MIM) estimation is to determine the initial marking(s) with a minimum total number of tokens in labeled Petri net models of real-world application systems in engineering. This paper addresses the MIM estimation under the observation of a label sequence. We aim to find all possible solutions while reducing the computational cost in solving this NP-hard problem by a hybrid evolutionary heuristic. Design/methodology/approach First, a set of transition firing sequences (TFSs) consistent with the observed label sequences is found to generate the initial population of the genetic algorithm (GA). Second, we calculate the initial markings that correspond to each TFS and are minimal, and the quality of individuals is assessed. Third, simulated annealing (SA) is used to improve the individuals in the population, and then GA is used for a global search. Finally, the above operations are repeated until the termination condition is satisfied. Findings In comparison with the existing methods for finding MIMs with heuristic algorithms, our algorithm improves 51.5-316.7% in the number of obtained MIMs. Moreover, the computation time is reduced by 90.1% compared with a classical algorithm. Originality/value In this paper, an effective evolutionary algorithm by hybridizing GA and SA is developed to estimate MIMs. The proposed approach can obtain the complete MIM set at a lower computational cost.

关键词： Discrete event systems Genetic algorithm hybrid evolutionary algorithm Initial marking estimation Labeled Petri nets Optimization Petri nets Simulated annealing

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An efficient hybrid evolutionary heuristic using genetic algorithm and simulated annealing algorithm to solve machine loading problem in FMS

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2009年第19期47卷 5421-5448页

作者： Yogeswaran, M. Ponnambalam, S. G. Tiwari, M. K. Monash Univ Sch Engn Petaling Jaya 46150 Selangor Malaysia Indian Inst Technol Dept Ind Engn & Management Kharagpur 721302 W Bengal India

In this paper, a machine loading problem in a flexible manufacturing system (FMS) is discussed, with bi-criterion objectives of minimising system imbalance and maximising system throughput in the occurrence of technological constraints such as available machining time and tool slots. A mathematical model is used to select machines, assign operations and the required tools in order to minimise the system's imbalance while maximising the throughput. An efficient evolutionary algorithm by hybridising the genetic algorithm (GA) and simulated annealing (SA) algorithm called GASA is proposed in this paper. The performance of the GASA is tested by using 10 sample dataset and the results are compared with the heuristics reported in the literature. The influence of genetic operators on the evolutionary search in GASA is studied and reported. Two machine selection heuristics are proposed and their influence on the quality of the solution is also studied. Extensive computational experiments have been carried out to evaluate the performance of the proposed evolutionary heuristics and the results are presented in tables and figures. The results clearly support the better performance of GASA over the algorithms reported in the literature.

关键词： flexible manufacturing system machine loading problem hybrid evolutionary algorithm

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hybrid evolutionary algorithms based on PSO and GA

Hybrid evolutionary algorithms based on PSO and GA

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Congress on evolutionary Computation (CEC)

作者： Shi, XH Lu, YH Zhou, CG Lee, HP Lin, WZ Liang, YC Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)0780378040

Inspired by the idea of genetic algorithm, this paper proposed two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.

关键词： particle swarm optimization genetic algorithm hybrid evolutionary algorithm

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Enhancing Deep Neural Network Performance in Diabetes Classification: A hybrid Optimization Approach with Kernel Similarity

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SN Computer Science 2025年第4期6卷 1-18页

作者： Priyadarshinee, Sudipta Panda, Madhumita School of Computer Science G.M. University Odisha Sambalpur India

This study proposed a new AutoML algorithm for diabetes classification using deep neural networks (DNNs)with hyperparameter optimization. evolutionary techniques, which were previously used to determine the ideal hyperparameter configuration for DNNs, can be computationally costly. This paper suggests a method for hyperparameter optimization that reduces computational costs while still achieving accurate validation of the error function. The proposed hybrid evolutionary method employs redundant hyperparameter points, while previous evolutionary optimizations calculate the discrepancy between the hyperparameter points, resulting in a computationally intensive approach. The novel hybrid optimization, called CCO, combines the coati optimization algorithm (COA) with computational automata (CA) to optimize hyperparameters for disease classification in a multi-level DNN. Another novel contribution in CCO is introduction of hyperparameter similarity kernel (HSK) which reduces the call to fitness function in constrained hyperspace. This hybrid evolutionary strategy-based algorithm searches for optimal setting via hyperparameter and the experiments show that this novel algorithm performs better than the state-of-the-art algorithms with reduction in computational complexity. Utilizing the PIMA dataset, CCDeepNet attains a testing accuracy of 95.73%, surpassing state-of-the-art models. The incorporation of HSK diminishes unnecessary hyperparameter evaluations, resulting in a 37.3% decrease in floating-point operations (FLOPs) than the baseline AutoML and a 32.59% reduction in model size relative to baseline DNNs. These developments enable CCDeepNet an excellent and effective option for practical medical diagnostics, achieving a balance between high accuracy and computational efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： AutoML Computational automata Diabetes classification hybrid evolutionary algorithm Hyperparameter optimization

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Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design

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ENGINEERING OPTIMIZATION 2008年第1期40卷 1-16页

作者： Jeong, S. Obayashia, S. Minemura, Y. Tohoku Univ Sendai Miyagi 9808577 Japan Toshiba Semicond Yokohama Kanagawa 2350017 Japan

A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem-the geometry of diesel engine combustion chamber reducing exhaust emissions such as NO., soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate that the volume near the lower central part of the combustion chamber has a large effect on exhaust emissions and the optimum chamber geometry will vary depending on fuel injection angle.

关键词： low exhaust emission combustion chamber design hybrid evolutionary algorithm data-mining self-organising map (SOM)

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