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An enhanced bat algorithm approach for reducing electrical power consumption of air conditioning systems based on differential operator

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APPLIED THERMAL ENGINEERING 2016年 99卷 834-840页

作者： Coelho, Leandro dos Santos Askarzadeh, Alireza Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Parana Brazil Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Energy saving plays a vital role in the decision-making process surrounding building design. Most often, the power consumption of chillers has a significant proportion of the total power consumption of the heating, ventilating and air conditioning (HVAC) systems. The problem of efficiently managing multiple chiller systems (MCSs) in HVAC is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. In this paper, an extended version of optimal chiller loading (OCL), namely, daily optimal chiller loading (DOCL) is introduced where a 24-h cooling load profile should be satisfied by a number of chillers so that the total power consumption of the chillers during 24-h is minimized. Then, an efficient optimization method is proposed for solving the DOCL by means of a new enhanced differential bat algorithm (DBA) which is a swarm intelligence paradigm. The simulation results represent that DBA produces promising results in comparison with other optimization metaheuristics, such as the original BA, firefly algorithm (FA), harmony search (HS), chicken swarm optimization (CSC), differential evolution (DE) and exponential natural evolution strategy (xNES). (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multiple chiller systems optimization Electrical power consumption bat algorithm Differential evolution

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Well placement optimization using metaheuristic bat algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2017年 150卷 348-354页

作者： Naderi, Meysam Khamehchi, Ehsan Amirkabir Univ Technol Fac Petr Engn Tehran Polytech Hafez Ave Tehran Iran

The design of an optimal field development and production management is a complicating task because of influencing various factors on decision-making process. Typical factors include number and type of wells, well locations, production constraints, economic factors like capital expenditure, operating costs, and oil sale price. The situation is further complicated due to the uncertainty associated with various effective engineering and geological parameters. In this study, three meta heuristics algorithms of genetic algorithm (GA), particle swarm optimization (PSO) and bat inspired algorithm (BA) are used for optimal determination of six production well locations. Net present value (NPV) is used as an objective function in optimization process. PUNQ-S3 benchmark model is simulated in MATLAB environment in order to search the entire complex reservoir during optimization. Next, the effectiveness of algorithms will be compared in terms of convergence rate and NPV improvement over iterations. The simulation results show that the BA is superior since it reduces the number of functional evaluations and thus improving the computational efficiency. In addition, the BA provides better NPV improvement over PSO and GA. The results indicate that the BA increases NPV by 7.5% and 21.7% over PSO and GA respectively.

关键词： Well placement optimization Net present value Genetic algorithm Particle swarm optimization bat algorithm

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Chaotic bat algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2014年第2期5卷 224-232页

作者： Gandomi, Amir H. Yang, Xin-She Univ Akron Dept Civil Engn Akron OH 44325 USA Middlesex Univ Sch Sci & Technol London NW4 4BT England

bat algorithm (BA) is a recent metaheuristic optimization algorithm proposed by Yang. In the present study, we have introduced chaos into BA so as to increase its global search mobility for robust global optimization. Detailed studies have been carried out on benchmark problems with different chaotic maps. Here, four different variants of chaotic BA are introduced and thirteen different chaotic maps are utilized for validating each of these four variants. The results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks. (C) 2013 Elsevier B.V. All rights reserved.

关键词： bat algorithm Chaos Metaheuristic Global optimization

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Optimal design of damage tolerant composite using ply angle dispersion and enhanced bat algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3387-3406页

作者： Reddy, M. Prashanth Mukherjee, Shuvajit Ganguli, Ranjan Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India

In this work, minimum weight optimization of laminated composite is performed using a newly developed enhanced bat algorithm (EBA). bat algorithm (BA) is a recently developed swarm-based optimization technique which is inspired by the echolocation behavior of bats. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, the EBA is used to perform the design optimization of laminated composites. The laminate analysis based on classical laminate theory is utilized for the stress calculations. Tsai-Wu failure curve is considered as the constraint in this constrained optimization problem. Number of plies at each orientation angle are considered as the design variables. The design optimization has been carried out for both conventional and unconventional (dispersed plies) stacking sequences considering different loading configurations: uniaxial tension, biaxial tension with and without shear loadings. Ply angles dispersed in the range of 5 circle\-85 circle 25 circle-65 circle and 0 circle\-90 circle} at intervals of 5 circle are considered for the unconventional stacking sequence to increase damage tolerance. In addition, a new mathematical function is proposed to measure the dispersion of ply angles in the laminate called the dispersion function. Also, the performance of EBA is compared with standard BA in the optimum weight design of composite laminates.

关键词： Laminated composite bat algorithm Tsai-Wu failure curve Constrained optimization Stacking sequence Damage tolerance

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Data Clustering Method Based on Improved bat algorithm With Six Convergence Factors and Local Search Operators

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IEEE ACCESS 2020年 8卷 80536-80560页

作者： Zhu, L. F. Wang, J. S. Wang, H. Y. Guo, S. S. Guo, M. W. Xie, W. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114051 Peoples R China

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

关键词： Clustering bat algorithm convergence factor hunting mechanism sinusoidal position update strategy

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A self-adaptive bat algorithm for the truck and trailer routing problem

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ENGINEERING COMPUTATIONS 2018年第1期35卷 108-135页

作者： Wang, Chao Zhou, Shengchuan Gao, Yang Liu, Chao Beijing Univ Technol Sch Econ & Management Beijing Peoples R China Qingdao Geotech Invest & Surveying Res Inst Qingdao Peoples R China

Purpose The purpose of this paper is to provide an effective solution method for the truck and trailer routing problem (TTRP) which is one of the important NP-hard combinatorial optimization problems owing to its multiple real-world applications. It is a generalization of the famous vehicle routing problem (VRP), involving a group of geographically scattered customers served by the vehicle fleet including trucks and trailers. Design/methodology/approach The meta-heuristic solution approach based on bat algorithm (BA) in which a local search procedure performed by five different neighborhood structures is developed. Moreover, a self-adaptive (SA) tuning strategy to preserve the swarm diversity is implemented. The effectiveness of the proposed SA-BA is investigated by an experiment conducted on 21 benchmark problems that are well known in the literature. Findings Computational results indicate that the proposed SA-BA algorithm is computationally efficient through comparison with other existing algorithms found from the literature according to solution quality. As for the actual computational time, the SA-BA algorithm outperforms others. However, the scaled computational time of the SA-BA algorithm underperforms the other algorithms. Originality/value In this work the authors show that the proposed SA-BA is effective as a method for the TTRP problem. To the authors' knowledge, the BA has not been applied previously, as in this work, to solve the TTRP problem.

关键词： Vehicle routing bat algorithm Self-adaptive Truck and trailer

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Parallel bat algorithm Applied to the Economic Load Dispatch Problem

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JOURNAL OF INTERNET TECHNOLOGY 2016年第4期17卷 761-769页

作者： Tsai, Cheng-Fu Thi-Kien Dao Pan, Tien-Szu Trong-The Nguyen Chang, Jui-Fang Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan

In this paper, a novel optimization algorithm based on parallel version of bat algorithm (PBA) with communication strategy is proposed to solve the numerical optimization problems and the economic load dispatch problem (ELD). The aim of the parallel bat algorithm with communication strategies is to correlate individuals in swarm and to share the computation load over few processors. Based on the original structure of the bat algorithm (BA), the bat populations are split into several independent groups. In addition, the communication strategy provides the information flow for the bats to communicate in different groups. In the experiment, a set of benchmark functions and the ELD are used to test the behavior of convergence, the accuracy, and the speed of the PBA method. According to the experimental results, this novel method with communicational strategy increases the accuracy of the BA on finding the better solution. Compared with the genetic algorithm (GA) method and the particle swarm optimization (PSO) method. The experimental results are shown that the proposed PBA method can provide the higher efficiency and accuracy.

关键词： Parallel bat algorithm Economic load dispatch bat algorithm

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Path Generation for a Given Performance Evaluation Value Interval by Modifying bat algorithm with Heuristic

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2023年第5期33卷 787-814页

作者： Wang, Fujun Cao, Zining Li, Zhen Xing, Chao Zong, Hui Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Minist Ind & Informat Technol Key Lab Safety Crit Software Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Collaborat Innovat Ctr Novel Software Technol & In Nanjing 211106 Peoples R China Sci & Technol Electroopt Control Lab Luoyang 471000 Peoples R China

Path generation means generating a path or a set of paths so that the generated path meets specified properties or constraints. To our knowledge, generating a path with the performance evaluation value of the path within a given value interval has received scant attention. This paper subtly formulates the path generation problem as an optimization problem by designing a reasonable fitness function, adapts the Markov decision process with reward model into a weighted digraph by eliminating multiple edges and non-goal dead nodes, constructs the path by using a priority-based indirect coding scheme, and finally modifies the bat algorithm with heuristic to solve the optimization problem. Simulation experiments were carried out for different objective functions, population size, number of nodes, and interval ranges. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Path generation performance evaluation heuristic algorithm bat algorithm

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Modified bat algorithm based on covariance adaptive evolution for global optimization problems

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SOFT COMPUTING 2018年第16期22卷 5215-5230页

作者： Shan, Xian Cheng, Huijin China Univ Petr Sch Sci Qingdao 266580 Peoples R China China Univ Petr Sch Econ & Management Qingdao 266580 Peoples R China

bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.

关键词： bat algorithm Swarm intelligence Covariance adaptive evolution

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Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2016年 31卷 829-841页

作者： Moraveji, Mostafa Keshavarz Naderi, Meysam Amirkabir Univ Technol Dept Chem Engn Tehran Polytech 424 Hafez Ave Tehran 158754413 Iran Amirkabir Univ Technol Dept Petr Engn Tehran Polytech 424 Hafez Ave Tehran 158754413 Iran

Rate of penetration (ROP) prediction is crucial for drilling optimization because of its role in minimizing drilling costs. There are many factors, which determine the drilling rate of penetration. Typical factors include formation properties, mud rheology, weight on bit, bit rotation speed, type of bit, wellbore inclination, and bit hydraulics. In this paper, first, the simultaneous effect of six variables on penetration rate using real field drilling data has been investigated. Response surface methodology (RSM) was used to develop a mathematical relation between penetration rate and six factors. The important variables include well depth (D), weight on bit (WOB), bit rotation speed (N), bit jet impact force (IF), yield point to plastic viscosity ratio (Y-p/PV), 10 min to 10 s gel strength ratio (10MGS/10SGS). Next, bat algorithm (BA) was used to identify optimal range of factors in order to maximize drilling rate of penetration. Results indicate that the derived statistical model provides an efficient tool for estimation of ROP and determining optimum drilling conditions. Sensitivity study using analysis of variance shows that well depth, yield point to plastic viscosity ratio, weight on bit, bit rotation speed, bit jet impact force, and 10 min to 10 s gel strength ratio have the greatest effect on ROP variation respectively. Cumulative probability distribution of predicted ROP shows that the penetration rate can be estimated accurately at 95% confidence interval. In addition, study shows that by increasing well depth, there is an uncertainty in selecting the jet impact force as the best objective function to determine the effect of hydraulics on penetration rate. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Drilling Penetration rate Response surface methodology bat algorithm

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