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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Amiri, Babak Hossain, Liaquat Crawford, John Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781450311786

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community detection problem this study used modified harmony search algorithm (HAS), the original HAS often converges to local optima which is a disadvantage with this method. To avoid this shortcoming the HAS was combined with a Chaotic Local search (CLS). In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique was used to control the size of the repository. The experiments in synthetic and real networks show that the proposed multi-objective community detection algorithm is able to discover more accurate community structures.

关键词： Complex Network Community Detection Multi-objective harmony search algorithm Chaos Local search

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A Novel Biogeography Inspired Trajectory-Following Controller for National Instrument Robot 3rd

A Novel Biogeography Inspired Trajectory-Following Controlle...

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3rd International Conference on New Trends in Information and Communications Technology Applications (NTICT)

作者： Saleh, Basma Jumaa al-Aqbi, Ali Talib Qasim Saedi, Ahmed Yousif Falih Al Mustansiriyah Univ Comp Engn Dept Baghdad Iraq

ISBN: (纸本)9783030016531;9783030016524

This paper is devoted to the design of a trajectory-following control for a differentiation nonholonomic wheeled mobile robot. It suggests a kinematic nonlinear controller steer a National Instrument mobile robot. The suggested trajectory-following control structure includes two parts;the first part is a nonlinear feedback acceleration control equation based on adaptive sliding mode control that controls the mobile robot to follow the predetermined suitable path;the second part is an optimization algorithm, that is performed depending on the Mutated harmony search algorithm to tune the parameters of the controller to obtain the optimum trajectory. The simulation is achieved based on MATLAB R2017b and the results present that the kinematic nonlinear controller with MHS algorithm is more effective and robust than the original harmony search learning algorithm;It is shown that the proposed scheme is robust to reduce the chattering problem because of adaptive control law of sliding mode controller;this is shown by the minimized tracking-following error to equal or less than (1 cm) and getting smoothness of the linear velocity less than (0.2 m/s), and all trajectory-following results with predetermined suitable are taken into account. Stability analysis of the suggested controller is proven using the Lyapunov method.

关键词： Trajectory-following mobile robot Sliding mode control Kinematic nonlinear controller National instrument harmony search algorithm

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An Improved Optimization algorithm Applied in Image Processing 32

An Improved Optimization Algorithm Applied in Image Processi...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Zhang, Ping Thomson, John Douglas Zhang, Yining Anshan Normal Univ Phys Dept Anshan 114005 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne Vic 3000 Australia

ISBN: (纸本)9781728158556

Aim to improve the default of harmony search (HS) algorithm which is easily trapping into local optima while doing global search. An improved optimization algorithm presented in this paper. Mining historical iteration information to set up an instructive dataset, then the global search mechanism is combined with a local search strategy. After that, an integer planning study based on distance strategy and the fuzzy constraint processing in spatial division of solution space. The proposed model combines with FCM applied in image processing. The experimental results show that the proposed method can get more accuracy and less CPU operation time versus FCM and s-FCM.

关键词： harmony search algorithm Image Processing FCM Local Optimization

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Model and Solution for Capacitated Facility Location Problem

Model and Solution for Capacitated Facility Location Problem

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24th Chinese Control and Decision Conference (CCDC)

作者： Yu, Hongtao Gao, Liqun Lei, Yanhua Shenyang Inst Engn Dept Automat Engn Shenyang 110136 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781457720727

This paper presents a new model for capacitated facility location problem, where serve radius and economic benefit are considered. In the new model, the objective is to maximize the total return investment. However, the objective for multiple knapsack problem is to maximize the total profit. So the capacitated facility location problem can be translated into multiple knapsack problem. Because this problem is difficult to solve, we propose a hybrid harmony search algorithm, which incorporates harmony search algorithm with greedy algorithm. Numerical results from computational experiments are presented and analyzed.

关键词： harmony search algorithm Capacitated Facility location Knapsack problem

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Thinly-distributed Cylindrical Array Antenna Optimization Based on Improved Genetic algorithm

Thinly-distributed Cylindrical Array Antenna Optimization Ba...

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9th International Conference on Signal and Image Processing (ICSIP)

作者： Ye, Shuhan Zhang, Jinwan Wu, Yuyi Lin, Jia Yan, Yuxin Sun Yat Sen Univ Elect & Commun Engn Shenzhen Peoples R China

ISBN: (纸本)9798350350920

In response to the urgent demand for lightweight, low-cost, and low-energy-consumption radar systems for unmanned aerial and ground vehicles, this study conducts research on sparse radar array optimization. Based on the relationship between array performance and array structure, an improved genetic algorithm is proposed. This optimization algorithm enhances the traditional genetic algorithm using a harmony search algorithm. By constructing an adaptive function, employing a hybrid crossover strategy, and utilizing perturbation updates, it seeks the optimal solution in a larger search space, optimizing array structure and deployment design. This is achieved by sparsifying part of the array elements from a uniformly spaced full array, forming a non-uniform array with element spacing constrained to integer multiples of half-wavelength. Compared to traditional genetic algorithms and other methods, the proposed approach achieves higher gain with fewer antenna elements, significantly suppresses sidelobes, reduces power consumption and cost, and obtains excellent performance indicators.

关键词： sparse radar array improved genetic algorithm(GA) harmony search algorithm

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Enhanced beetle antenna search: a swarm intelligence algorithm

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Asian Journal of Civil Engineering 2021年第6期22卷 1185-1219页

作者： Yousif, S. Saka, M.P. Department of Civil Engineering University of Bahrain P.O. Box: 32038 Isa Town Bahrain

This study presents a novel enhancement to one of the recent swarm intelligence technique called beetle antenna search (BAS) algorithm. The enhanced algorithm is named (eBAS) and it allows population of beetles as oppose to single beetle as it was the case in the original BAS algorithm. Consideration of population of beetles brings in another capability, which is to avoid getting stuck at local optimum solutions. The other improvements are also suggested in the determination sensing length and step size. The performance of the proposed algorithm is compared with nine metaheuristic algorithms selected from the literature in addition to the original BAS. The algorithms selected are Bat algorithm (BAT), Biogeography-Based Optimization (BBO), Artificial Bee Colony (ABC), Particle Swarm Optimizer (PSO), Cuckoo’s search (CS), Harmonic search (HS), Differential Evolution (DE), Teaching–Learning Based Optimization (TLBO), and Fire-Fly algorithm (FFA). With each algorithm, 5 optimization examples having continuous and/or discrete variables are solved ranging from 5 design variables’ optimization problem to 23 variables’ problem with several constraints. In addition, 2 real size problems with 10 and 15 design variables are solved using eBAS, ABC, and DE algorithm. Each problem is set to run ten times with different random seed values at each run with all the algorithms to investigate the robustness of the newly developed algorithm. The main performance criteria considered are the best, median, average, and worst results attained, alongside the convergence speed of the average result towards known global minimum solution. It has been shown that eBAS has not only enhanced the original BAS performance but also outperformed the other metaheuristic algorithms at all optimization problems considered. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Artificial bee colony algorithm Beetle antenna search Differential evolution harmony search algorithm Metaheuristic algorithms Particle swarm optimizer Swarm intelligence

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Furnace Temperature Prediction of Aluminum Smelting Furnace Based on KPCA-ELM

Furnace Temperature Prediction of Aluminum Smelting Furnace ...

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Chinese Automation Congress (CAC)

作者： Huang, Qinghao Lei, Sining Jiang, Chenglong Xu, Chenhua Guangxi Univ Sch Elect Engn Nanning Peoples R China

ISBN: (纸本)9781728113128

Furnace temperature influencing factors are complicated in regenerative aluminum smelting furnace. Traditional modeling ways of neutral networks are complex and their generalization abilities are poor. Therefore, this paper used the method of Kernel Principal Component Analysis (KPCA) to extract the main components of networks inputs. KPCA can reduce the relevance of inputs and simplify network structure. The improved extreme learning machine (ELM) is adapted as the prediction model of furnace temperature. ELM improved by harmony search algorithm (HS) can effectively enhance the performance of ELM. In the end, the furnace temperature prediction model was established by KPCA-HS-ELM were compared with the model were established by BP, ELM and HS-ELM. Results show that the prediction model of furnace temperature based on KPCA-HSELM has better prediction ability and higher generalization ability. The KPCA-HS-ELM model can provide reference for furnace temperature control.

关键词： regenerative aluminum smelting furnace furnace temperature prediction kernel principal component analysis harmony search algorithm extreme learning machine

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Feature Selection of Support Vector Machine based on Harmonious Cat Swarm Optimization 7

Feature Selection of Support Vector Machine based on Harmoni...

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7th IEEE International Conference on Ubi-Media Computing and Workshops (U-MEDIA)

作者： Lin, Kuan-Cheng Mang, Kai-Yuan Hung, Jason C. Natl Chung Hsing Univ Dept Management Informat Syst Taichung Taiwan Overseas Chinese Univ Dept Informat Management Taichung Taiwan

ISBN: (纸本)9781479942664

Cat Swarm Optimization algorithm (CSO) is an optimization algorithm which proposed in 2006. Indicated by previous studies, CSO has good performance. We proposed a method to improve CSO and presenting a modified CSO named Harmonious-CSO (HCSO). The method is changing the concept of cat alert surroundings in seeking mode of CSO. We change the formula of seeking mode and add a concept of HS algorithm. In this paper, we use Support Vector Machine (SVM) be classifier combine with feature selection to verify the performance of algorithm. For the experimental results, the HCSO algorithm has a better solution than CSO.

关键词： cat swarm optimization harmony search algorithm feature selection SVM

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Metaheuristic Optimization on Conventional Freeman Chain Code Extraction algorithm for Handwritten Character Recognition 1

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9th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Mohamad, Muhammad A. Haron, Habibollah Hasan, Haswadi Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Johor Malaysia

ISBN: (数字)9783319544724

ISBN: (纸本)9783319544724

In Handwritten Character Recognition (HCR), interest in feature extraction has been on the increase with the abundance of algorithms derived to increase the accuracy of classification. In this paper, a metaheuristic approach for feature extraction technique in HCR based on harmony search algorithm (HSA) was proposed. Freeman Chain Code (FCC) was used as data representation. However, the FCC representation is dependent on the route length and branch of the character node. To solve this problem, the metaheuristic approach via HSA was proposed to find the shortest route length and minimum computational time for HCR. At the end, comparison of the result with other metaheuristic approaches namely, Differential Equation (DE), Particle Swarm Optimization (PSO), Genetic algorithm (GA) and Ant Colony Optimization (ACO) was performed.

关键词： harmony search algorithm Freeman chain code Handwritten character recognition

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AUTOMATED WAVELET-BASED FAULT DETECTION AND DIAGNOSIS FOR SMART DISTRIBUTION SYSTEMS AND MICROGRIDS

AUTOMATED WAVELET-BASED FAULT DETECTION AND DIAGNOSIS FOR SM...

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作者： Tamer Sayed Abdelhamid Abdelgayed Sewilam University of Ontario

学位级别：博士

The legacy electric power system is defined as a one-way power flow from a centralized power generation plant to customers (consumers). In the smart distribution systems, the customers are allowed to produce electricity through the distributed energy resources (e. g. solar photovoltaics), as well as to consume energy, hence, the smart distribution systems can be defined as a two-way power flow. The Micro-Grid system is defined as a part of the smart distribution system that may include distributed energy resources, energy storage systems and loads. In addition, the Micro-Grid system can operate in two modes, grid-connected or non-grid-connected (i. e., islanded mode). The protection of the Micro-Grid system represents one of the major operational challenges, in particular when considering the integration of distributed energy resources, which may result in different fault current levels, especially in islanding mode. However, the capability of protection system equipment to be more accurate and dependable for faults diagnostic in the Micro-Grid is considered a challenge until now. In this thesis, an automated wavelet-based fault detection and diagnosis technique based on a combination of Wavelet Transform, harmony search algorithm, and Machine Learning approaches is developed for fault diagnosing in the Micro-Grid systems. The harmony search algorithm as an optimization technique is used to identify the optimum wavelet function(s) and the optimum wavelet decomposition level(s) to extract the most prominent features that are hidden in the current/voltage waveforms when applying the discrete wavelet transform. This is unlike previous works in which only one arbitrary wavelet function is used based on a trial and error process. In order to automate the fault classification process in Micro-Grid system, and to examine the effectiveness of the automated wavelet-based fault detection and diagnosis method against other approaches, two machine learning techniques (i. e. Dec

关键词： Micro-grid distributed energy resources fault diagnosis fault protection feature extraction wavelet transforms harmony search algorithm machine learning

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