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Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields

JOURNAL OF APPLIED REMOTE SENSING 2015年第1期9卷

作者： Sun, Xu Yang, Lina Gao, Lianru Zhang, Bing Li, Shanshan Li, Jun Chinese Acad Sci Inst Remote Sensing & Digital Earth Key Lab Digital Earth Sci Beijing 100094 Peoples R China Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Guangdong Peoples R China

Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing;however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC MRF-cluster showed good stability. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： hyperspectral remote sensing cluster artificial bee colony algorithm

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An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期43卷 1340-1345页

作者： Liao, Xiang Zhou, Jianzhong Zhang, Rui Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

In this paper, we present a novel adaptive artificial bee colony (AABC) algorithm and compare its efficiency with other existing algorithms for long-term dispatch of cascaded hydropower systems. We formulate the long-term economic dispatch of hydropower systems as a complicated nonlinear optimization problem with a group of complex constraints. We analyze the performances of three different values of the control parameter modification rate (MR) in the AABC. We modify the employed bee phase to improve the global optimal capability of the AABC algorithm, and utilize a novel probabilistic method to enhance the search ability of the onlooker bee phase. Furthermore, we change the scout bee phase to avoid local maxima. We demonstrate the performance of the AABC algorithm and compare it with other algorithms using the data from hydropower systems of Three Gorges in China. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch artificial bee colony algorithm Adaptive Constrained optimization Swarm intelligence Cascaded hydropower system

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An interval-based multi-objective artificial bee colony algorithm for solving the web service composition under uncertain QoS

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JOURNAL OF SUPERCOMPUTING 2019年第9期75卷 5622-5666页

作者： Seghir, Fateh Khababa, Abdallah Semchedine, Fouzi Univ Setif 1 Fac Technol Dept Basic Studies Technol Setif 19000 Algeria Univ Setif 1 Fac Sci Dept Comp Sci Setif 19000 Algeria Univ Setif 1 Inst Opt & Precis Mech IOMP Setif 19000 Algeria

Most of the existing works addressing the QoS-aware service composition problem (QoSSCP) are based on the assumption of fixed quality of service (QoS) characteristics of elementary web services. However, in the real world, some QoS criteria may be imprecise for many unexpected factors and conditions. Therefore, when dealing with QoSSCP, we must consider the uncertain proprieties of QoS. Moreover, very few studies propose multi-objective solutions for solving the QoSSCP, and there is no multi-objective algorithm solving the QoSSCP under uncertain QoS, in which the non-deterministic values of the QoS attributes are expressed as interval numbers. To resolve this issue, we formulate an interval-constrained multi-objective optimization model to the QoSSCP, and we propose a novel interval-based multi-objective artificial bee colony algorithm (IM_ABC) to solve the suggested model. To deal with the interval-valued of objective functions, we define an uncertain constrained dominance relation for ordering solutions in which the performance and stability are simultaneously considered. As inspired by Deb's feasibility handling constraints, a new interval-based feasibility technique is proposed to deal with interval constraints. In order to control the diversity of the non-dominated solutions obtained by IM_ABC, the original crowding distance of NSGA-II is extended and adopted to the uncertain QoSSCP by incorporating to it a new interval distance definition. Based on real-world and random datasets, the effectiveness of the proposed IM_ABC has been verified through multiple experiments, where the comparison results demonstrates the superiority of IM_ABC compared to the recently proposed interval-based multi-objective optimization algorithms IPMOPSO, IPMOEOA, and MIIGA as well as a recently introduced interval-based fuzzy ranking single-objective GAP approach.

关键词： Web service composition QoS uncertainty Interval number Multi-objective optimization artificial bee colony algorithm

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Application of artificial bee colony algorithm for particle size distribution measurement of suspended sediment based on focused ultrasonic sensor

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第7期43卷 1680-1690页

作者： Li, Yeming Xia, Yidan Xie, Dailiang Xu, Ya Xu, Zhipeng Wang, Yuebing China Jiliang Univ Key Lab Flow Measurement Technol Zhejiang Prov Hangzhou 310018 Zhejiang Peoples R China

A new focused ultrasonic sensor is proposed, based on which the measurement system for particle size distribution measurement of suspended sediment is established. Compared with the traditional ultrasonic sensors, the one used in this paper is equipped with piezoelectric transducer (PZT) on an arc-shaped shell, to concentrate ultrasonic beams on one measurement point. The sensor is used to measure the particle size distribution of suspended sediment. The experiments were carried out on water-sediment mixtures with different particle size distribution. Due to multiple parameters and non-linearity of the ultrasonic attenuation model, the artificial bee colony (ABC) inversion algorithm is used to estimate particle size distribution, thus improving measurement accuracy. The particle sizes obtained by sieving method are seen as reference values. The results indicate that whether the suspended particles are subject to a unimodal distribution, uniform distribution or random distribution, the particle size distribution obtained by ABC inversion algorithm is consistent with the result obtained by the sieve method. The results demonstrate that the method has good utility and accuracy within the low concentration range.

关键词： Solid-liquid two-phase flow ultrasonic particle size distribution artificial bee colony algorithm

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Multi-strategy-based artificial bee colony algorithm for AUV path planning with angle constraints

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Xu, Jian Han, Zhiyong Yin, Liangang Yan, Zheping Yu, Yuyang Ma, Guangzhi Harbin Engn Univ Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China

This paper focuses on a path planning problem of an autonomous underwater vehicle (AUV) traversing target points at desired angles in an obstacle environment with eddy currents. A tangent-spatial partition method for path planning model construction is developed, which guarantees the direction of arrival in addition to extending the scope of waypoint search. An improved artificial bee colony algorithm integrating with multiple evolutionary strategies (ABC-MES) is proposed. More specifically, a population initialization method that follows the best point set principle is presented to increase population diversity and accelerate convergence. A multi-strategy evolutionary approach is developed in the employed bees phase to enhance population quality and establish a decision support database for subsequent upper confidence bound (UCB) learning. The UCB algorithm is then applied in the onlooker bee stage for assessment and screening of the optimal evolutionary strategy according to the accumulation of prior knowledge and the exploration of new knowledge. Finally, the T-distribution and reversal learning are explored in the scout bee stage to update old nectar sources to prevent the algorithm from falling into the local optimal prematurely. The strong ability of the proposed ABC-MES algorithm to jump out of the local optima is ensured by comparing it with 9 existing algorithms in terms of accuracy and stability with the help of 28 test functions. Simulation results reveal that the path generated by the proposed ABC-MES algorithm has lower overall cost accounting for time efficiency, navigation distance and energy consumption.

关键词： artificial bee colony algorithm Path planning Multi-strategy co-evolution Upper confidence bound algorithm

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Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

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AUTOMATION IN CONSTRUCTION 2014年第Mar.期38卷 14-29页

作者： Yahya, M. Saka, M. P. Univ Bahrain Sakhir Bahrain Minist Municipal Affairs & Urban Planning Manama Bahrain Univ Bahrain Dept Civil Engn Sakhir Bahrain

Construction site layout planning has been recognized as a critical step in construction planning. The basic function of this process is to find the best arrangement of the temporary facilities according to multiple objectives that may conflict with each other and subjected to logical and resource constraints. The formulation of the construction site layout planning problem as an optimization problem turns out to be a nonlinear programming problem where there are conflicting multi-objectives to be achieved. It is shown that the swarm intelligence based meta-heuristic algorithms are quite powerful in obtaining the solution of such hard to solve type of optimization problems. In this study a multi objective artificial bee colony (MOABC) via Levy flights algorithm is proposed to determine the optimum construction site layout. The model is intended to optimize the dynamic layout of unequal-area under two objective functions. The performance of MOABC with Levy flights is demonstrated on a real benchmark construction engineering of construction site layout planning problem and the optimum solution obtained is compared with the one determined by the ant colony algorithm. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Construction site layout planning Swarm intelligence Levy flights artificial bee colony algorithm

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Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood

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INFORMATION SCIENCES 2015年 316卷 180-200页

作者： Gao, Weifeng Chan, Felix T. S. Huang, Lingling Liu, Sanyang China Univ Petr Qingdao 266580 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Xidian Univ Sch Sci Xian 710071 Peoples R China

Concerning this issue that the solution search equation of artificial be colony algorithm (ABC) does well in exploration but badly in exploitation, a bare bones ABC with parameter adaptation and fitness-based neighborhood is proposed, BABC for short reference. The proposed method employs a Gaussian search equation to produce a new candidate individual at the onlooker phase which exploits the valuable information hidden in the best individual to improve the exploitation, while, at the employed bee phase, a parameter adaptation strategy and a fitness-based neighborhood mechanism are integrated into the search equation which can take advantage of the information from the previous search and better individuals to enhance the search ability. The proposed framework can be applied to any ABC with minimal changes. Furthermore, the proposed framework is applied to the original ABC and several highly regarded ABC variants. The comparison results demonstrate that the proposed framework is able to significantly improve the performance of the original ABC and the modified ABC variants. (C) 2015 Elsevier Inc. All rights reserved.

关键词： artificial bee colony algorithm Gaussian search equation Parameter adaptation strategy Fitness-based neighborhood mechanism

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An improved artificial bee colony algorithm and its application to reliability optimization problems

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APPLIED SOFT COMPUTING 2018年 62卷 736-767页

作者： Ghambari, Soheila Rahati, Amin Univ Sistan & Baluchestan Dept Comp Sci Zahedan Iran

artificial bee colony (ABC) algorithm is a well-established swarm optimization technique that has been successfully applied for solving different kinds of optimization problems. In spite of its efficiency and wide use, ABC still suffers from slow convergence speed. To overcome this insufficiency, an improved version of ABC algorithm called IABC has been proposed in this paper. First, the proposed IABC incorporates a probabilistic population size reduction mechanism in order to accelerate the convergence speed. This mechanism transfers high quality solutions to the next cycle of the algorithm and discards the rest. Second, in addition to the original search operator of ABC, the IABC utilizes a new search operator which enhances the exploitation capability. This new search operator generates a new solution based on a randomly selected pair of solutions and the current best solution. Third, to better balance the trade-off between exploration and exploitation, the IABC unifies the employed and onlooker bee phases into an improved bee phase by using a self-adaptive probabilistic selection scheme. This helps the IABC to decide either to apply the original or the new search operator to produce a new solution. The performance of IABC is evaluated against CEC2014 test suite and eight well-known reliability optimization problems. Numerical experiments indicate that the IABC provides competitive results compared to several state-of-the-art algorithms in terms of convergence speed, robustness, and solution accuracy. Moreover, the IABC considerably improves the best-known solution for one reliability optimization problem. (C) 2017 Elsevier B.V. All rights reserved.

关键词： artificial bee colony algorithm Population size reduction mechanism CEC2014 test suite Reliability optimization problems

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Two modified versions of artificial bee colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2013年 225卷 601-609页

作者： Alizadegan, Amir Asady, Babak Ahmadpour, Mohammad Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Islamic Azad Univ Arak Branch Dept Comp Engn Arak Iran Islamic Azad Univ Maku Branch Dept Math Maku Iran

artificial bee colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm. (C) 2013 Published by Elsevier Inc.

关键词： Swarm intelligence Optimization algorithms artificial bee colony algorithm Numerical function optimization Unconstrained optimization

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A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint

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SOFT COMPUTING 2017年第5期21卷 1193-1202页

作者： Sundar, Shyam Suganthan, P. N. Jin, Chua Tay Xiang, Cai Tian Soon, Chong Chin Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Singapore Inst Mfg Technol 71 Nanyang Dr Singapore 638075 Singapore

This paper studies a hybrid artificial bee colony (ABC) algorithm for finding high quality solutions of the job-shop scheduling problem with no-wait constraint (JSPNW) with the objective of minimizing makespan among all the jobs. JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. ABC algorithm is a swarm intelligence technique based on intelligent foraging behavior of honey bee swarm. The proposed hybrid approach effectively coordinates the various components of ABC algorithm such as solution initialization, selection and determination of a neighboring solution with the local search in such a way that it leads to high quality solutions for the JSPNW. The proposed approach is compared with the two best approaches in the literature on a set of benchmark instances. Computational results show the superiority of the proposed approach over these two best approaches.

关键词： Scheduling Job-shop No-wait artificial bee colony algorithm Swarm intelligence

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