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Mobile Robot Wall-Following Control by Improved artificial bee colony algorithm to design a Compensatory Fuzzy Logic Controller 14

Mobile Robot Wall-Following Control by Improved Artificial B...

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2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Chen, C. Du, H. Lin, S. National Key Laboratory of Science and Technology on Micro/Nano Fabrication Peking University Beijing China

ISBN: (纸本)9781538604496

This dissertation proposes an improved artificial bee colony (IABC) algorithm for designing a compensatory fuzzy logic controller (CLFC) in order to achieve an actual mobile robot wall-following task. During the wall -following task, the CFLC inputs measure the distance between the ultrasonic sensors and the wall, and the outputs of the CFLC are the robot's left-wheel and right-wheel speeds. A cost function is defined to evaluate the performance of the CFLC in the wall -following task. The cost function indudes three control factors (CF) which are defined as follows: maintaining a user-defined robot-wall distance, avoiding robot-wall collision, and ensuring that the robot can successfully negotiate the venue. The original artificial bee colony algorithm (ABC) simulates the intelligent foraging behavior of honey-bee swarms, which are good at exploration but poor at exploitation. An improved ABC algorithm, the IABC algorithm, is proposed that adopts the mutation strategies of differential evolution to balance exploration and exploitation. The IABC algorithm applies a new reward-based roulette wheel selection where an obtained a better solution by gains a reward during the learning stage. To demonstrate the performance of the IABC designed CFLC, the method was compared with other population-based algorithms with respect to the efficiency of the wall-following task.

关键词： Wall following control robot motion control fuzzy control artificial bee colony algorithm rifferential evolution algorithm

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Parallel Optimization Based on artificial bee colony algorithm 2

Parallel Optimization Based on Artificial Bee Colony Algorit...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Debo Li Yongxin Feng Jun Zhong Jielian Zhou Libao Yin Junhao Zhou Electric Power Research Institute of Guangdong Power Grid Corporation

ISBN: (纸本)9781509036202

This paper aims to tackle the shortcomings of the standard artificial bee colony algorithm(ABC) such as slow convergence,long solving time and being easy to fall into local *** study the state transformation formula and propose a parallelized ABC algorithm with Message Passing Interface(MPI).We use the traveling salesman problem(TSP) as the case *** experiments show that the parallel ABC algorithm has an advantage in speed over the standard algorithm *** and convergence speed.

关键词： artificial bee colony algorithm traveling salesman problem parallel optimization combinatorial optimization swarm intelligence

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A hybrid artificial bee colony algorithm for the p-median problem with positive/negative weights

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OPSEARCH 2017年第1期54卷 67-93页

作者： Jayalakshmi, B. Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

In the classical p-median problem, the objective is to find a set Y of p vertices on an undirected graph G = (V, E) in such a way that Y subset of V and the sum of distances from all the vertices to their respective closest vertices in Y is minimized. In this paper, we have considered the weighted case where every vertex in G has either a positive or a negative weight under two different objective functions, viz. the sum of the minimum weighted distances and the sum of the weighted minimum distances. In this paper, we have proposed a hybrid artificial bee colony (ABC) algorithm for the positive/ negative weighted p-median problem where each solution generated by ABC algorithm is improved by an interchange based randomized local search. In addition, an interchange based exhaustive local search is applied on some of the best solutions obtained after the execution of ABC algorithm in a bid to further improve their quality. We have compared our approach with the state-of-theart approaches available in the literature on the standard benchmark instances. Computational results demonstrate the effectiveness of our approach.

关键词： artificial bee colony algorithm Swarm intelligence p-Median problem p-Median problem with positive negative weights

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Assessment of Distribution System Reliability using artificial bee colony algorithm 2

Assessment of Distribution System Reliability using Artifici...

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2nd IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT)

作者： Dixit, Mukul Kundu, Prasanta Jariwala, Hitesh R. SV Natl Inst Technol Dept Elect Engn Surat 395007 India

ISBN: (纸本)9781509032396

This paper addresses the usefulness of artificial bee colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

关键词： artificial bee colony algorithm distribution system reliability particle swarm optimization algorithm reliability indices

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Research on Global artificial bee colony algorithm Based on Crossover 8

Research on Global Artificial Bee Colony Algorithm Based on ...

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8th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhang, Pinghua AnHui Xinhua Univ AnHui Publ Secur Coll Sch Informat Engn Hefei Anhui Peoples R China

ISBN: (纸本)9781538604977

In order to overcome the shortcomings of artificial bee colony algorithm including slow convergence speed, easily falling into local optimum value, neglect of development and other issues, Mechanism of other bionic intelligent optimization algorithms, A new algorithm of Global artificial bee colony algorithm based on crossover which can effectively improve the convergence rate, enhance the development of the algorithm and the global optimization ability is proposed, and the algorithm can effectively avoid the local optimum. Finally, the Seven standard test functions are selected to carry out the experiment and simulation. The results show that the convergence speed and accuracy of the proposed algorithm (CGABC) are significantly improved compared with other algorithms such as ABC algorithm, GABC algorithm and so on.

关键词： cross global artificial bee colony algorithm intelligent algorithm

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Fire Detection Method of Mine Belt Conveyor Based on artificial bee colony algorithm 29

Fire Detection Method of Mine Belt Conveyor Based on Artific...

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第29届中国控制与决策会议

作者： Liu Yuxin Ma Xianmin School of Electronics and Control Xi'an University of Science and Technology

ISBN: (纸本)9781509046584

The detection of fire on mine belt conveyor is very difficult in traditional image processing method,a novel image processing method is proposed in this paper,which integrates artificial bee colony algorithm,gray scale morphology and information *** artificial bee colony algorithm the best threshold is approached in parallel via the division of labor,cooperation and information sharing of employed bees,onlookers and *** fitness function of artificial bee colony algorithm is designed by 2 D maximum entropy method and fire image thresholds are regarded as nectar *** order to reduce image noise the close operation is applied based on gray scale *** analysis and simulation experimental results indicate that the proposed method is useful to detect fire of mine belt conveyor in complex coal under ground environment.

关键词： Mine Fire Detection Belt Conveyor Image Processing artificial bee colony algorithm

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Fractional Order Butterworth Filter Design using artificial bee colony algorithm 7

Fractional Order Butterworth Filter Design using Artificial ...

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7th International Symposium on Embedded Computing and System Design (ISED)

作者： Dwivedi, Atul Kumar Bhatt, Sandeep Kumar Ghosh, Subhojit Bhilai Inst Technol Durg India Natl Inst Technol Raipur Madhya Pradesh India

ISBN: (纸本)9781538630327

Fractional order representation has been more effective in analyzing various physical systems more efficiently as compared to conventional integer order representation. Fractional order representation allows higher order integer systems to be replaced by small fractional order equivalent systems. In this paper, fractional order filters are designed using Swarm intelligence based evolutionary optimization algorithm. The designed filters have been compared with other state of the art evolutionary optimization techniques. In order to evaluate the order reduction by the proposed technique the designed filters are converted to equivalent higher order digital filter. The applicability of the designed filters for real time applications has been validated using TMS320F2812 DSP processor.

关键词： Fractional order filter Low passes Butterworth filter artificial bee colony algorithm Fractance device Analog filter design

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Research on Neighborhood Search Strategy of artificial bee colony algorithm for Satisfiability Problems 10

Research on Neighborhood Search Strategy of Artificial Bee C...

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10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Guo, Ying Zhang, Changsheng Ningxia Inst Technol Coll Elect & Informat Engn Shizuishan Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781538636756

The neighborhood search process plays an important role in artificial bee colony algorithm. Aiming at the problems caused by ignoring the characteristics of a given problem, the neighborhood search strategy for satisfiability problems is studied. To balance the ability of global exploration and local search, the BIR and RIB neighborhood selection strategies are proposed, and four new solution generation strategies are compared and studied. The experimental results show that, compared with the original method, the proposed strategies have improved in varying degrees in performance for stochastic SAT problems.

关键词： artificial bee colony algorithm satisfiability problems neighborhood selection new solution generation

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Applied artificial bee colony algorithm for multiple Capacitated Vehicle Routing Problem: Case study of the plastic packaging industry 14

Applied Artificial Bee Colony Algorithm for multiple Capacit...

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2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Mingprasert, S. Masuchun, R. King Mongkuts Inst Technol Ladkrabang Dept Ind Engn Fac Engn Bangkok Thailand

ISBN: (纸本)9781538604496

The aim of this research is to investigate the transportation problem of a plastic packaging industry case study because at present, competition within the plastic packaging business requires good services and on time delivery. Therefore, customer complaints and delayed deliveries are major problems for this industry group and that is why it is required to create more reliability and satisfaction for customers. Nowadays, this industry engages a transport supplier for goods delivery but it still has high transportation cost problems due to vehicle routing management. artificial bee colony algorithm is applied to overcome this issue. This algorithm is verified by using Vehicle Routing Problem benchmark (VRP) in terms of minimized total traveling distance. Then, for presenting high efficiency, this algorithm was validated for good performance compared with other algorithms. After algorithm validation, artificial bee colony algorithm is employed to solve the vehicle routing problem that has various vehicle capacities in the plastic packaging industry case study. The experimental results show that the purposed algorithm can be used to tackle the transportation problem in terms of minimizing total traveling distance and transportation costs, and also enhancing quality service levels and vehicle utilization for the multi-capacitated Vehicle Routing Problem.

关键词： capacitated vehicle routing problem artificial bee colony algorithm plastic packaging industry

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Multi-population Based Search Strategy Ensemble artificial bee colony algorithm with a Novel Resource Allocation Mechanism 24th

Multi-population Based Search Strategy Ensemble Artificial B...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Wu, Liu Sun, Zhiwei Zhang, Kai Li, Genghui Wang, Ping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Shenzhen Polytech Sch Comp Engn Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9783319700939;9783319700922

artificial bee colony algorithm (ABC) is a simple yet effective biologically-inspired optimization method for global numerical optimization problems. However, ABC often suffers from slow convergence due to its solution search equation performs well in exploration but badly in exploitation. Moreover, all food sources are assigned with almost equal computing resources so that good solutions are not being fully exploited. In order to address these issues, we propose a multi-population based search strategy ensemble ABC algorithm with a novel resource allocation mechanism (called MPABC_RA). Specifically, in employed bee phase, all food sources are divided into three subgroups according to their quality. Then each subgroup uses different search equations to find better solutions. By this way, better tradeoff between exploitation and exploration can be obtained. In addition, the superior solutions in onlooker bee phase are allocated with more resources to evolve. And onlooker bees fully exploit the area between the locations of the selected superior solutions and the current best solution by a novel search equation. We compare MPABC_RA with four state-of-the-art ABC variants on 22 benchmark functions, the experimental results show that MPABC_RA is significantly better than the compared algorithms on most test functions in terms of solution accuracy, convergence rate and robustness.

关键词： artificial bee colony algorithm Multi-population Search strategy ensemble Resource allocation Global numerical optimization

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