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A Bird Flock Gravitational search algorithm Based on the Collective Response of Birds

COMPUTER JOURNAL 2017年第11期60卷 1687-1716页

作者： Han, XiaoHong Qiang, Yan Lan, Yuan Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan Shanxi Peoples R China

Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass which is widely used nowadays for efficient solution of optimization problems. For the purpose of enhancing the performance of original GSA, this paper proposes a new GSA called Bird Flock Gravitational search algorithm (BFGSA) based on the collective response of birds. Although GSA performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main major steps including initialization, identification of the nearest neighbors and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original particle swarm optimization and state-of-the-art GSA algorithms. Furthermore, we applied BFGSA to a real-world application of data clustering. The results show that BFGSA improves the performance of the original GSA and obtains the best results compared with our selected GSA-type algorithms in benchmarking experiments and clustering experiments.

关键词： gravitational search algorithm learning algorithm heuristic search algorithm collective behavior numerical function optimization data clustering

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Parallel Simulated Annealing with a Greedy algorithm for Bayesian Network Structure Learning

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第6期32卷 1157-1166页

作者： Lee, Sangmin Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.

关键词： Simulated annealing Markov processes Greedy algorithms Bayes methods search problems Convergence Instruction sets Bayesian networks structure learning heuristic search algorithm parallel structure learning memoization simulated annealing with a greedy algorithm

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Research on optimization operation technology of QT oil pipeline based on the heuristic algorithm

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ENERGY REPORTS 2022年 8卷 10134-10143页

作者： Liu, Enbin Lai, PinRong Peng, Yong Chen, Qikun Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

Global environmental problems have become increasingly prominent, and China, as one of the world's major powers, should take action. China promises to achieve a "carbon peak " by 2030, and carbon dioxide emissions will no longer increase, and will gradually decrease after reaching the peak. To achieve "carbon neutrality " by 2060, all the carbon dioxide emissions will be offset by tree planting, energy saving and emission reduction. The optimization of pipeline energy consumption is also associated with it. In recent years, the transportation mode of the QT oil pipeline has changed from normal temperature transportation to heating transportation. The energy consumption of this transportation method mainly comes from heating furnaces and pumps. In order to reduce energy consumption and find a suitable pipeline operation plan, this article optimizes and analyzes the transformed QT oil pipeline under the premise of ensuring safe production. Based on programming software, this article establishes a corresponding mathematical model of energy consumption of the QT oil pipeline, and uses artificial bee colony algorithm, invasive weed algorithm and optimization algorithm based on biogeography to solve the model. The article innovatively introduces the speed of the variable frequency pump as a variable to study the energy consumption optimization problem of the oil pipeline, analyze the practical application of the oil pipeline of the QT oil pipeline, and obtains the best plan for the optimized operation of the oil pipeline of the QT oil pipeline It provided research basis and played a role in promoting the country's dual-carbon goals. (c) 2022 The Authors. Published by Elsevier Ltd.

关键词： Crude oil pipeline Optimization heuristic search algorithm Energy consumption Carbon peak Carbon neutral

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Accurate Reliability Boundary Evaluation of Approximate Arithmetic Circuit

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2022年第10期30卷 1507-1518页

作者： Wang, Zhen Zhang, Guofa Liu, Peng Ye, Jing Jiang, Jianhui Shanghai Univ Elect Power Sch Comp Sci & Technol Shanghai 200090 Peoples R China Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Tongji Univ Sch Software Engn Shanghai 200092 Peoples R China

Approximate arithmetic circuit (AAC) has emerged as a promising high-performance and energy-efficient circuit paradigm, which can be used in many applications with inherent error tolerance. To guarantee the usability of AACs and the availability of resilient applications, it is necessary to analyze the reliability of AACs. Most current literature focus on the error characteristics of AACs and few methods can be applied to estimate the reliability of AACs. These methods mostly have exponential time complexities and evaluate the average reliability assuming the input combinations are equally likely. In reality, the primary input (PI) signals can be given with any probability from 0 to 1. In this article, we assume that the PIs have random signal probabilities and propose approaches to reliability boundary estimation for AACs. First, we propose a new efficient and accurate method to evaluate the reliability of AACs. The method mainly calculates the AAC reliability for an input vector set, and furthermore, during the calculation, the correlation problem is considered to increase accuracy. Then, based upon the proposed AAC reliability evaluation method, we present the approaches to finding the reliability boundary. Randomly given signal probabilities of every PI, two heuristic search algorithms are utilized to find the lowest reliability. A comparison of the results on three series of AACs and the circuits in the EvoApprox8b library confirms that the proposed reliability evaluation method is more accurate and efficient than the previous method. Further experiments verify the plausibility of the calculated reliability boundary of AACs.

关键词： Reliability Integrated circuit reliability Integrated circuit modeling Circuit faults Adders Correlation Arithmetic Approximate arithmetic circuit (AAC) correlation coefficient heuristic search algorithm reliability boundary signal reliability

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Optimization algorithm based on kinetic-molecular theory

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Journal of Central South University 2013年第12期20卷 3504-3512页

作者：范朝冬欧阳红林张英杰艾朝阳 College of Electrical and Information Engineering Hunan University College of Information Science and Engineering Hunan University Institute of Cognitive Science Hunan University

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

关键词： optimization algorithm heuristic search algorithm kinetic-molecular theory diversity convergence

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PSOHS: an efficient two-stage approach for data clustering

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MEMETIC COMPUTING 2013年第2期5卷 155-161页

作者： Hatamlou, Abdolreza Hatamlou, Masoumeh Islamic Azad Univ Khoy Branch Tehran Iran Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimizat Res Grp Bangi 43600 Selangor Malaysia Tarbiat Moallem Univ Tehran Iran

Cluster analysis is an important task in data mining and refers to group a set of objects such that the similarities among objects within the same group are maximal while similarities among objects from different groups are minimal. The particle swarm optimization algorithm (PSO) is one of the famous metaheuristic optimization algorithms, which has been successfully applied to solve the clustering problem. However, it has two major shortcomings. The PSO algorithm converges rapidly during the initial stages of the search process, but near global optimum, the convergence speed will become very slow. Moreover, it may get trapped in local optimum if the global best and local best values are equal to the particle's position over a certain number of iterations. In this paper we hybridized the PSO with a heuristic search algorithm to overcome the shortcomings of the PSO algorithm. In the proposed algorithm, called PSOHS, the particle swarm optimization is used to produce an initial solution to the clustering problem and then a heuristic search algorithm is applied to improve the quality of this solution by searching around it. The superiority of the proposed PSOHS clustering method, as compared to other popular methods for clustering problem is established for seven benchmark and real datasets including Iris, Wine, Crude Oil, Cancer, CMC, Glass and Vowel.

关键词： Data clustering Particle swarm optimization heuristic search algorithm

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Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第6期37卷 8063-8076页

作者： Amali, D. Geraldine Bessie Dinakaran, M. Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Vellore Inst Technol Sch Informat Technol Vellore Tamil Nadu India

This paper proposes a new metaheuristic global optimization algorithm inspired by Wildebeest herding behavior called Wildebeest Herd Optimization (WHO) algorithm. WHO algorithm mimics the way nomadic Wildebeest herds search vast areas of grasslands efficiently for regions of high food density. The WHO algorithm models five principal Wildebeest behaviors: firstly Wildebeests have limited eyesight and can only search for food locally, secondly Wildebeests stick to the herd to escape predators, thirdly Wildebeest herd as a whole migrates to regions of high food availability based on historical knowledge of annual grass growth rates and rainfall patterns, fourthly Wildebeests move out of crowded overgrazed regions and finally Wildebeests move to avoid starvation. The WHO algorithm is compared to Physics inspired, Swarm based, Biologically inspired and Evolution inspired global optimization algorithms on an extended test suite of benchmark optimization problems including rotated, shifted, noisy and high dimensional problems. Extensive simulation results indicate that the WHO algorithm proposed in this paper significantly outperforms state-of-the-art popular metaheuristic optimization algorithms like Particle Swarm Optimization algorithm (PSO), Genetic algorithm (GA), Gravitational search algorithm (GSA), Artificial Bee Colony algorithm (ABC) and Simulated Annealing (SA) on shifted, high dimensional and large search range problems.

关键词： Global optimization Wildebeest Herd Optimization algorithm biologically inspired metaheuristic heuristic optimization heuristic search algorithm

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Disruption: A new operator in gravitational search algorithm

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SCIENTIA IRANICA 2011年第3期18卷 539-548页

作者： Sarafrazi, S. Nezamabadi-pour, H. Saryazdi, S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

To improve the exploration and exploitation abilities of the standard Gravitational search algorithm (GSA), a novel operator called "Disruption", originating from astrophysics, is proposed. The disruption operator is inspired by nature and, with the least computation, has improved the ability of GSA to further explore and exploit the search space. The proposed improved GSA has been evaluated on 23 nonlinear benchmark functions and compared with standard GSA, the genetic algorithm and particle swarm optimization. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Numerical function optimization heuristic search algorithm Swarm intelligence Gravitational search algorithm Disruption operator

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Gases Brownian Motion Optimization: an algorithm for Optimization (GBMO)

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APPLIED SOFT COMPUTING 2013年第5期13卷 2932-2946页

作者： Abdechiri, Marjan Meybodi, Mohammad Reza Bahrami, Helena Qazvin Azad Univ Elect Comp & IT Dept Qazvin Iran Amirkabir Univ Technol Comp Engn & Informat Technol Dept Tehran Iran

In recent years, different optimization methods have been developed for optimization problem. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new algorithm for optimization inspired by the gases brownian motion and turbulent rotational motion is introduced, which is called Gases Brownian Motion Optimization (GBMO). The proposed algorithm is created using the features of gas molecules. The proposed algorithm is an efficient approach to search and find an optimum solution in search space. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various functions. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Brownian Motion Turbulent Rotational Motion Evolutionary algorithm Optimization algorithm heuristic search algorithm Kinetic Energy Satisfiability Problem Multimodal Function Unimodal Function Ideal Gas Law

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Efficient algorithms for ordinary differential equation model identification of biological systems

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IET SYSTEMS BIOLOGY 2007年第2期1卷 120-129页

作者： Gennemark, P. Wedelin, D. Chalmers Univ Technol Dept Comp Sci & Engn SE-41296 Gothenburg Sweden

algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.

关键词： noise model selection Biology and medical computing physiological models differential equations General, theoretical, and mathematical biophysics model identification parameter estimation Numerical approximation and analysis ordinary differential equation Model reactions in molecular biophysics metabolic pathway molecular biophysics biological systems biology computing biochemistry heuristic search algorithm identification algorithms Differential equations (numerical analysis) Function theory, analysis Simulation, modelling and identification genetics genetic network

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