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Object recognition with the hybrid evolutionary algorithm and response analysis in security applications

OPTICAL ENGINEERING 2004年第10期43卷 2292-2302页

作者： Maslov, IV Gertner, I CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA CUNY City Coll Dept Comp Sci New York NY 10031 USA

The recognition of an object in a scene is a common and important task of electronic imaging arising in many defense and security applications. When the image of the sought object is significantly distorted, and the image of the scene is cluttered, noisy, and contains many objects, the commonly used methods based on correlation and comparison of the feature vectors of the images can show poor performance. The approach utilizing the particular model of the hybrid evolutionary algorithm based on image response analysis is proposed to solve the object recognition problem formulated as the global optimization problem. The computational experiments with two-dimensional grayscale images show that the proposed approach can solve complex object recognition problems. It is able to discriminate between objects having a high degree of similarity, and to detect the sought object in the large cluttered and multi-object scene. (C) 2004 Society of Photo-Optical Instrumentation Engineers.

关键词： Object recognition Security applications hybrid evolutionary algorithm Response analysis

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Mathematical modeling and a hybrid evolutionary algorithm for process planning

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JOURNAL OF INTELLIGENT MANUFACTURING 2021年第3期32卷 781-797页

作者： Liu, Qihao Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Process planning is an essential part of the manufacturing system linking the designing and practical manufacturing. However, the reported process planning models are too simple to describe all characteristics because of the complexity of process planning. Therefore, a new mixed-integer linear programming (MILP) mathematical model is established based on OR-node of the network graph. In the model, the linear expression of the OR-node controlling function as well as three types of changing costs are first established. Beside, considering the OR-node selection state in the encoding and decoding method, a hybrid evolutionary algorithm (HEA) is designed to combine a genetic algorithm with a simulated annealing algorithm. The tournament selection method is adopted in the proposed HEA, and the discussion on the tournament size is conducted on the open problems to make the algorithm designing more reasonable and scientific. The HEA and the new MILP model are both tested on series of numerical experiments which are carried on the existing benchmarks as well as some randomly generated cases. The behavior of both two methods can verify their effectiveness and superiority successfully.

关键词： Process planning Mixed-integer linear programming model hybrid evolutionary algorithm Benchmark

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A GPU implementation of a hybrid evolutionary algorithm: GPuEGO

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JOURNAL OF SUPERCOMPUTING 2014年第2期70卷 684-695页

作者： Garcia-Martinez, J. M. Garzon, E. M. Ortigosa, P. M. Univ Almeria Dept Informat CeiA3 Almeria Spain

The high computation requirements of global optimization algorithms, when used to solve real optimization problems, have caused the appearance of different parallelization strategies using several parallel computing architectures. In this work, the Universal evolutionary Global Optimizer is implemented in CUDA to be run on GPU architectures (GPuEGO). This parallelization of the referred evolutionary multimodal optimization algorithm is rather different from other previous parallel implementations designed to be executed into shared or distributed memory processors. In this case, due to the special characteristics of a GPU architecture, the original data structures are not valid and it has been necessary to redefine them and all the functions that operate with them. When this approach is applied the acceleration factors achieved by GPuEGO range from 6.33 to 23.20 depending on the test function.

关键词： Global optimization hybrid evolutionary algorithm GPU computing

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NERS_HEAD: a new hybrid evolutionary algorithm for solving graph coloring problem

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SOFT COMPUTING 2023年第17期27卷 12117-12131页

作者： Guo, Ping Guo, Bin Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Key Lab Software Theory & Technol Chongqing 400044 Peoples R China

The graph coloring problem is an NP-hard problem. Currently, one of the most effective methods to solve this problem is a hybrid evolutionary algorithm. This paper proposes a hybrid evolutionary algorithm NERS_HEAD with a new elite replacement strategy. In NERS_HEAD, a method to detect the local optimal state is proposed so that the evolutionary process can jump out of the local optimal state by introducing diversity on time;a new elite structure and a replacement strategy are designed to increase the diversity of the evolutionary population so that the evolution process can not only converge quickly but also jump out of the local optimal state in time. The comparison experiments with current excellent graph coloring algorithms on 59 DIMACS benchmark instances show that NERS_HEAD can effectively improve the efficiency and success rate of solving graph coloring problems.

关键词： Graph coloring problem hybrid evolutionary algorithm Tabu search Combinatorial optimization

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Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm

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ENERGY 2013年 54卷 129-138页

作者： Doagou-Mojarrad, Hasan Gharehpetian, G. B. Rastegar, H. Olamaei, Javad Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Elect Engn Tehran Iran

This paper presents an interactive fuzzy satisfying method, which is based on hybrid Modified Shuffled Frog Leaping algorithm, and should solve the problem of the Multi-objective optimal placement and sizing of DG (distributed generation) units in the distribution network. Minimizing total electrical energy losses, total electrical energy cost and total pollutant emissions produced are the objective functions in this problem. Also, the improvement of the voltage profile is considered as a constraint in determining the optimal placement. In the proposed method, the objective functions are modeled with fuzzy sets. The multi-objective problem is transformed into a mini-max problem, which is then handled by the proposed evolutionary algorithm. Finally, the proposed algorithm is tested on a 69-bus distribution test system based on technical, economical and environmental protection considerations. The simulation results illustrate the good performance and applicability of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Optimal placement of DG Sizing of DG Multi-objective problems hybrid evolutionary algorithm

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A hybrid evolutionary algorithm for the capacitated minimum spanning tree problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 144卷 1页

作者： Lu, Yongliang Benlic, Una Wu, Qinghua Fuzhou Univ Sch Econ & Management Fuzhou 350116 Peoples R China Tesco PLC Lever Bldg85 Clerkenwell Rd London EC1R 5AR England Huazhong Univ Sci & Technol Sch Management 1037 Luoyu Rd Wuhan Peoples R China

The capacitated minimum spanning tree (CMST) problem is a fundamental problem in telecommunication network design. Given a central node and a set of remote terminal nodes with specified demands for traffic, the goal of CMST is to find a minimum cost spanning tree (network) such that the traffic on any arc of the network satisfies the capacity constraint. To tackle this NP-hard problem, we propose an effective hybrid evolutionary algorithm that integrates a backbone-based crossover operator, a destroy-and-repair mutation operator to generate meaningful offspring solutions, and an intensification-driven adaptive tabu search procedure that considers both feasible and infeasible solutions in search of high-quality local optima. Extensive computational results on a set of 126 well-known CMST benchmark instances from the literature indicate that the proposed algorithm competes favorably with the state-of-the-art heuristics. For a selection of 25 most challenging CMST instances with unknown optimal solutions, the proposed algorithm reports new upper bounds (improved best-known solutions) in 9 cases, while reaching the best-known result for 12 instances. Furthermore, we provide experimental analyses to identify the key algorithmic features of the proposed approach.

关键词： Heuristics Capacitated minimum spanning tree Tabu search hybrid evolutionary algorithm

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A tabu search based hybrid evolutionary algorithm for the max-cut problem

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APPLIED SOFT COMPUTING 2015年 34卷 827-837页

作者： Wu, Qinghua Wang, Yang Lu, Zhipeng Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

This paper presents a tabu search based hybrid evolutionary algorithm (TSHEA) for solving the max-cut problem. The proposed algorithm integrates a distance-and-quality based solution combination operator and a tabu search procedure based on neighborhood combination of one-flip and constrained exchange moves. Comparisons with leading reference algorithms from the literature disclose that the proposed algorithm discovers new best solutions for 15 out of 91 instances, while matching the best known solutions on all but 4 instances. Analysis indicates that the neighborhood combination and the solution combination operator play key roles to the effectiveness of the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Max-cut Metaheuristics hybrid evolutionary algorithm Tabu search

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A hybrid evolutionary algorithm for multiobjective variation tolerant logic mapping on nanoscale crossbar architectures

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APPLIED SOFT COMPUTING 2016年 38卷 955-966页

作者： Zhong, Fugui Yuan, Bo Li, Bin Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230026 Peoples R China Univ Sci & Technol China USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230026 Peoples R China

Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation tolerant logical mapping problem is treated as a bilevel multiobjective optimization problem. Since variation mapping is an NP-complete problem, a hybrid multiobjective evolutionary algorithm is designed to solve the problem adhering to a bilevel optimization framework. The lower level optimization problem, most frequently tackled, is modeled as the min-max-weight and min-weight-gap bipartite matching (MMBM) problem, and a Hungarian-based linear programming (HLP) method is proposed to solve MMBM in polynomial time. The upper level optimization problem is solved by evolutionary multiobjective optimization algorithms, where a greedy reassignment local search operator, capable of exploiting the domain knowledge and information from problem instances, is introduced to improve the efficiency of the algorithm. The numerical experiment results show the effectiveness and efficiency of proposed techniques for the variation tolerant logical mapping problem. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Nanoscale crossbar Variation tolerant logic mapping Bilevel multiobjective optimization Hungarian based linear programming hybrid evolutionary algorithm

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A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2017年 76卷 1-17页

作者： Teylo, Luan de Paula, Ubiratam Frota, Yuri de Oliveira, Daniel Drummond, Lucia M. A. Fluminense Fed Univ Inst Comp Niteroi RJ Brazil Univ Fed Rural Rio de Janeiro UFRRJ Rio De Janeiro Brazil

A growing number of data- and compute-intensive experiments have been modeled as scientific workflows in the last decade. Meanwhile, clouds have emerged as a prominent environment to execute this type of workflows. In this scenario, the investigation of workflow scheduling strategies, aiming at reducing its execution times, became a top priority and a very popular research field. However, few work consider the problem of data file assignment when solving the task scheduling problem. Usually, a workflow is represented by a graph where nodes represent tasks and the scheduling problem consists in allocating tasks to machines to be executed at a predefined time aiming at reducing the makespan of the whole workflow. In this article, we show that the scheduling of scientific workflows can be improved when both task scheduling and the data file assignment problems are treated together. Thus, we propose a new workflow representation, where nodes of the workflow graph represent either tasks or data files, and define the Task Scheduling and Data Assignment Problem (TaSDAP), considering this new model. We formulated this problem as an integer programming problem. Moreover, a hybrid evolutionary algorithm for solving it, named HEA-TaSDAP, is also introduced. To evaluate our approach we conducted two types of experiments: theoretical and practical ones. At first, we compared HEA-TaSDAP with the solutions produced by the mathematical formulation and by other works from related literature. Then, we considered real executions in Amazon EC2 cloud using a real scientific workflow use case (SciPhy for phylogenetic analyses). In all experiments, HEA-TaSDAP outperformed the other classical approaches from the related literature, such as Min-Min and HEFT. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Clouds Combinatorial optimization Task scheduling Data assignment hybrid evolutionary algorithm

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A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems

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SOFT COMPUTING 2017年第20期21卷 5975-5987页

作者： Wang, Hongfeng Fu, Yaping Huang, Min Huang, George Wang, Junwei Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China

In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.

关键词： evolutionary multi-objective optimization hybrid evolutionary algorithm Multi-objective optimization problem Particle swarm optimization Differential evolution Multi-population

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