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Web page classification based on a simplified swarm optimization

APPLIED MATHEMATICS AND COMPUTATION 2015年 270卷 13-24页

作者： Lee, Ji-Hyun Yeh, Wei-Chang Chuang, Mei-Chi Korea Adv Inst Sci & Technol Grad Sch Culture Technol Taejon 305701 South Korea Natl Tsing Hua Univ Dept Ind Engn & Engn Management Integrat & Collaborat Lab Hsinchu 300 Taiwan

Owing to the incredible increase in the amount of information on the World Wide Web, there is a strong need for an efficient web page classification to retrieve useful information quickly. In this paper, we propose a novel simplified swarm optimization (SSO) to learn the best weights for every feature in the training dataset and adopt the best weights to classify the new web pages in the testing dataset. Moreover, the parameter settings play an important role in the update mechanism of the SSO so that we utilize a Taguchi method to determine the parameter settings. In order to demonstrate the effectiveness of the algorithm, we compare its performance with that of the well-known genetic algorithm (GA), Bayesian classifier, and K-nearest neighbor (KNN) classifiers according to four datasets. The experimental results indicate that the SSO yields better performance than the other three approaches. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Web page classification simplified swarm optimization Taguchi method

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A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期49卷 414-429页

作者： Azizipanah-Abarghooee, Rasoul Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran

This paper presents a novel approach to depict the practical constraints of generator units such as reserve constraints, prohibited operating zones and valve-point effects in the optimal dynamic dispatch problem. Determining the power generation output of units at minimum total fuel cost a dynamic environment with ramp rate limits to satisfy load demand and transmission losses is too complicated and has a lot of local optima in its search space. In addition, the proposed problem has a non-linear, non-convex, non-smooth, multi-modal, non-separable, and non-differentiable nature. In order to overcome above problems a new hybrid technique, based on bacterial foraging and simplified swarm optimization algorithms combined with a new mutation operator and opposition-based initialization is proposed to restraint the premature convergence of the solutions. Therefore, the bacteria in chemo-tactic procedure are moved in short or long steps as well as swimming movements. Furthermore, to increase the diversity of the solution of the search space a novel self-adaptive mutation strategy which profits from four mutation rules is implemented. For more validation the simulation results are applied on four small, medium and large scale systems with 5-unit, 10-unit, 30-unit and 100-unit and compared with those of other methods in the area. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Bacterial foraging Hybrid evolutionary algorithm Optimal dynamic dispatch Practical constraints Self adaptive mutation strategy simplified swarm optimization

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Multi-objective simplified swarm optimization with weighting scheme for gene selection

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APPLIED SOFT COMPUTING 2018年 65卷 58-68页

作者： Lai, Chyh-Ming Natl Def Univ Management Coll Inst Resources Management & Decis Sci Taipei 112 Taiwan

Gene selection can be regarded as a multi-objective problem which involves both minimizing the size of a gene subset and maximizing the prediction performance. This work proposes a hybrid filter/wrapper method for gene selection based on multi-objective optimization. In this method, an emerging aggregate filter method is adopted as a filter with which to choose the most informative genes;in addition, a multi-objective simplified swarm optimization (MOSSO) is proposed and integrated with a support vector machine as a wrapper to seek an optimal gene subset from the selected genes. Unlike most current multi-objective based methods employed to handle gene selection problems, the proposed MOSSO uses a weighting scheme to guide the search towards the interesting regions as defined by the preference, which means that not all Pareto optimal solutions are generated, but only the ones gene selection prefers. The proposed method is validated using ten gene expression datasets, and the corresponding results are compared with those obtained with existing works. Statistical analysis indicates that the proposed method is highly competitive and, can be considered a promising alternative for dealing with gene selection problems. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Gene expression dataset Gene selection Multi-objective optimization Aggregate filter method simplified swarm optimization

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simplified swarm optimization with initialization scheme for dynamic weapon-target assignment problem

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APPLIED SOFT COMPUTING 2019年 82卷

作者： Lai, Chyh-Ming Wu, Tsung-Hua Natl Def Univ Management Coll Inst Resources Management & Decis Sci Taipei 112 Taiwan

The dynamic weapon-target assignment (DWTA) problem is a critical issue in the field of military operations research. The problem is highly constrained;thus, the use of an evolutionary method to solve the DWTA problem often encounters a population of infeasible solutions resulting in prohibitive computational burden. Aiming at accelerating the solution process and improving the solution quality, this work proposes an improved simplified swarm optimization called SSODT with two novel schemes: the deterministic initialization scheme, and the target exchange scheme. The deterministic initialization scheme is used in population initialization and utilizes problem-specific knowledge of DWTA to speed up the convergence of SSODT by generating a promising feasible solution which has a greater potential for evolving globally. The target exchange scheme is a local search updating feasible solutions in a manner that exchanges their variables without violating the engagement feasibility to enhance the exploitation capability of SSODT. The proposed method is empirically verified on thirty-six artificial problems and compared with widely popular evolutionary methods. The results demonstrate that the proposed SSODT is better than its competitors in terms of both solution quality and efficiency. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Dynamic weapon-target assignment problem simplified swarm optimization Feasible solution

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A simplified swarm optimization algorithm to minimize makespan on non-identical parallel machines with unequal job release times under non-renewable resource constraints

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OPERATIONAL RESEARCH 2024年第2期24卷 1-27页

作者： Chen, Jianfu Li, Kai Chu, Chengbin Sahli, Abderrahim Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China Univ Gustave Eiffel ESIEE Paris COSYS GRETTIA F-77454 Marne La Vallee France

This article studies a uniform parallel machine scheduling problem with unequal job release times. It is assumed that each machine consumes a certain non-renewable resource when manufacturing jobs. The objective is to find an optimal schedule to minimize the makespan, given that the total resource consumption does not exceed the given limit. A mathematical model is first built to derive optimal solutions for small-scale instances. For large-scale instances, a simplified swarm optimization (SSO) algorithm is proposed. Considering that the parameters of meta-heuristic algorithms have great impacts on the output solution, the Taguchi method is then applied to tune the algorithm parameters. Afterward, a large number of simulation experiments are conducted. Finally, Friedman's test and Wilcoxon signed-rank test are employed to analyze the simulation results from statistical perspectives. Experimental results reveal that the proposed algorithm can provide competitive solutions.

关键词： Parallel machine scheduling Resource consumption simplified swarm optimization Makespan

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Using particle-based simplified swarm optimization to solve the cold-standby reliability of the gas turbine industry

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第9期15卷 4456-4465页

作者： Singla, Shakuntla Kaur, Komalpreet Maharishi Markandeshwar MMEC Dept Math & Humanities Ambala India

simplified swarm optimization (SSO) and particle swarm optimization (PSO) are two types of modern swarm intelligence techniques that are often used for optimization. In order to identify the most effective system RRAP with a cold-standby strategic plan while aiming to exploit the reliability of the organization, the article discusses a PSSO procedure that combines UM of PSO and simplified swarm optimization, PSSO is especially impressive in comparison with other recently incorporated algorithms into four popular applications, namely a sequences scheme, a complex organization, a series-parallel system, and an airspeed indicator defense system for a turbine, with extensive experiments conducted on the pretty standard and well-known four benchmarks of reliability-redundancy allocation problems. Finally, the experiment findings show that the particle-based simplified swarm optimization can successfully solution to address the reliability-redundancy allocation (RRAP) issues using the cold-standby method and performs well in terms of organization reliability, even though the best platform consistency is not attained in all four benchmarks and experiment is done using python and Google colab.

关键词： simplified swarm optimization Particle swarm optimization Updates mechanisms

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Using simplified swarm optimization on path planning for intelligent mobile robot 9

Using simplified swarm optimization on path planning for int...

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9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception (ICSCCW)

作者： Eyer, Yoney Kirsal Near East Univ Software Engn Dept Fac Engn Mersin 10 TR-99138 Nicosia North Cyprus Turkey

This study examined how path planning of intelligent mobile robot could be enhanced by utilizing simplified swarm optimization (SSO) in working environment with irregular obstacles. The conceptual framework of this study was driven from an inspiration of communal behavior of birds flocking and fish schooling. This conceptual framework was supported by swarm intelligence, which is one of the famous research areas in the field of computational swarm intelligence such as particle swarm optimization (PSO) algorithm. Significant observations have been made that mobile robots are significantly affected by path planning problems, and solutions are established how to tackle these problems and numerous weaknesses. Therefore, this study proposes an effective solution which can yield a high quality and efficient mobile robots. The SSO technique was adapted in order to provide an effective solution to the discussed weaknesses. The designed simulation algorithm results showed that SSO does not have assemble in the closed work interface where no path between the initial and the destination points. Obtained results show that when the particle's path gets into an obstacle area, it is automatically repositioned to an obstacle free area. Autonomy and energy efficiency within the particles are also discussed. (c) 2018 The Authors. Published by Elsevier B.V

关键词： swarm intelligence path planning particle swarm optimization intelligent mobile robot simplified swarm optimization

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A simplified swarm optimization for Object Tracking

A Simplified Swarm Optimization for Object Tracking

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International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Guang Chung, Yuk Ying Yeh, Wei-Chang Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Natl Tsing Hua Univ Dept Ind Engn & Engn Management POB 24-60 Hsinchu 300 Taiwan

ISBN: (纸本)9781509006199

Moving object tracking in video sequences is an important task in the field of computer vision. In this paper, we propose a new population-based algorithm namely simplified swarm optimization (SSO) for tracking arbitrary objects. In SSO, the object model is first projected into a high-dimensional feature space, then the particles will fly over image pixels to find an optimal match of the target. While searching for the optimum, SSO progressively analyzes the occlusion situation. If any occlusion or disappearance of the target object is detected, the movement rules for the searching particles will be adaptively adjusted to recapture the target object. Experimental results showed that the SSO can robustly track an arbitrary target in various challenging conditions. Furthermore, SSO is capable to have 40% faster in speed and 36% higher in accuracy rate than the traditional PSO for varied environment.

关键词： object tracking particle swarm optimization simplified swarm optimization

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Solving Single Row Facility Layout Problem With simplified swarm optimization 13

Solving Single Row Facility Layout Problem With Simplified S...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Yeh, Wei-Chang Lai, Chyh-Ming Ting, Hsin-Yi Jiang, Yunzhi Huang, Hsin-Ping Natl Tsing Hua Univ Dept Ind Engn & Engn Management 101 Sec 2Kuang Fu Rd Hsinchu 30013 Taiwan Natl Def Univ Management Coll Inst Resources Management & Decis Sci Taipei 112 Taiwan Guangdong Polytech Normal Univ Sch Math & Syst Sci Guangzhou Guangdong Peoples R China Taoyuan Gen Hosp Minist Hlth & Welf Taoyuan Taiwan

ISBN: (纸本)9781538621653

Single Row Facility Layout Problem (SRFLP) is a NP-Complete permutation problem which has been widely studied in the academic field in order to improve the efficiency on production site. The main purpose of SRFLP is to find an optimal permutation of a given number of rectangular facilities with minimum total cost by arranging them along a straight line. In this paper, we use the simplified swarm optimization algorithm with an effective local search to find the near-optimal solution for SRFLP. The comparison of computational results of benchmark problems with previous studies shows that our proposed method has better efficiency and effectiveness in solving SRFLP.

关键词： simplified swarm optimization Single row facility problem permutation optimization particle swarm optimization genetic algorithm

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simplified swarm optimization for Repairable Redundancy Allocation Problem in Multi-state Systems with Bridge Topology

Simplified Swarm Optimization for Repairable Redundancy Allo...

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International Joint Conference on Neural Networks

作者： Wei-Chang Yeh Siang-Tai Wang Chyh-Ming Lai Yen-Cheng Huang Yuk Ying Chung Jsen-Shung Lin Department of IEEM National Tsing Hua University School of Information Technology University of Sydney Department of Information Management Central Police University

ISBN: (纸本)9781509006212

In recent decades, the redundancy allocation problem (RAP) is becoming an increasingly important issue in the initial stages of planning, designing, and controlling of systems. However, RAP in multi-state system (MSSs) still has some restrictions that components have only two performances: perfect functionality and complete failure. Therefore, this paper formulates a new kind of RAP called repairable redundancy allocation problem (RRAP) in multi-state systems (RRAP in MSSs) to break the restrictions. RRAP in MSSs is nearer to reality and widespread in many critical systems. RRAP in MSSs is not only an NP-hard problem, but also a nonlinear integer optimization problem. In order to deal with RRAP in MSSs in a reasonable time, this paper employs an optimization method of soft computing called simplified swarm optimization (SSO). Finally, the results obtained by SSO have been compared with that obtained from genetic algorithm (GA). Computational results show that the SSO is very competitive and effective in this problem.

关键词： Redundancy allocation problem Multi-state systems Repairable components Universal generating function simplified swarm optimization

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