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A novel model predictive control scheme based on bees algorithm in a class of nonlinear systems: Application to a three tank system

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NEUROCOMPUTING 2015年 152卷 294-304页

作者： Sarailoo, Morteza Rahmani, Zahra Rezaie, Behrooz SUNY Binghamton Dept Elect & Comp Engn Binghamton NY 13902 USA Babol Univ Technol Dept Elect & Comp Engn Intelligent Syst Res Grp Babol Sar *** Iran

This paper proposes a novel algorithm for utilizing bees algorithm in a model predictive control (MPC) in order to control a class of nonlinear systems. The bees algorithm is utilized in order to solve the open loop optimization problem (OOP), and it is based on the foraging behavior of honey bees. The proposed algorithm makes use of the bees algorithm for minimizing a predefined cost function in order to find the best input signals subject to constraints and a model of the system. The class of systems considered in this paper includes autonomous nonlinear systems without delay and with continuous and discrete inputs. The proposed algorithm is validated by simulating a three tank system as a case study. A comparison between the proposed novel MPC with different predictive horizons and a conventional MPC demonstrates the potential advantages of the proposed method such as reduction in computation time, good convergence toward desired values and ability of control management. Simulations also show the simplicity of applying and efficiency of the proposed algorithm for designing an MPC based on the bees algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： bees algorithm Intelligent algorithm Model predictive control Optimal control Optimization technique Three tank system

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A multi-objective supply chain optimisation using enhanced bees algorithm with adaptive neighbourhood search and site abandonment strategy

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SWARM AND EVOLUTIONARY COMPUTATION 2014年第0期18卷 71-82页

作者： Yuce, Baris Mastrocinque, Ernesto Lambiase, Alfredo Packianather, Michael S. Duc Truong Pham Cardiff Univ Sch Engn BRE Ctr Sustainable Construct Queens Bldg Cardiff CF24 3AA S Glam Wales Univ London Sch Management Royal Holloway Egham Surrey England Univ Salerno Dept Ind Engn Fisciano Italy Cardiff Univ Sch Engn Inst Mech & Mfg Engn Cardiff CF24 3AA S Glam Wales Univ Birmingham Sch Mech Engn Birmingham W Midlands England

In this paper, an enhanced version of the bees algorithm is proposed in dealing with multi-objective supply chain model to find the optimum configuration of a given supply chain problem in order to minimise the total cost and the total lead-time. The new bees algorithm includes an adaptive neighbourhood size change and site abandonment (ANSSA) strategy which is an enhancement to the basic bees algorithm. The supply chain case study utilised in this work is taken from literature and several experiments have been conducted in order to show the performances, the strength, the weaknesses of the proposed method and the results have been compared to those achieved by the basic bees algorithm and Ant Colony optimisation. The results show that the proposed ANSSA-based bees algorithm is able to achieve better Pareto solutions for the supply chain problem. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Supply chain management Multi-objective optimisation Swarm-based optimisation bees algorithm Adaptive neighbourhood search Site abandonment

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Optimal design of plate-fin heat exchangers by a bees algorithm

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APPLIED THERMAL ENGINEERING 2014年第1-2期69卷 267-277页

作者： Zarea, Hossein Kashkooli, Farshad Moradi Mehryan, Abdollah Mansuri Saffarian, Mohammad Reza Beherghani, Esmaeel Namvar Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

In this study, the application of bees algorithm (BA) in the optimum design of a cross flow plate-fin heat exchanger with offset strip fin is investigated. First, heat exchanger is optimized and designed according to the effectiveness optimization. Then, an analysis based on the second law of thermodynamics and minimizations of entropy generation units is performed. Specific heat duty, space restriction and permitted pressure drop are considered as the constraints for maximizing the effectiveness and minimizing the entropy generation units. Hot and cold flow length of the heat exchangers, number of fin layers, fin frequency, fin height, fin strip length and fin thickness are introduced as optimization variables. The effectiveness and accuracy of the suggested algorithm are compared with literature. The results have shown that BA can find optimum configuration with higher accuracy in comparison with Genetic algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive algorithm (ICA) and preliminary design. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Offset strip fin Optimization bees algorithm

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Integrating artificial bee colony and bees algorithm for solving numerical function optimization

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 635-651页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei 106 Taiwan Natl Taiwan Univ Sci & Technol Ecol & Hazard Mitigat Engn Researching Ctr Taipei 106 Taiwan

This work integrates artificial bee colony (ABC) and bees algorithm (BA) to develop a two bees (TB) algorithm. Agents of TB are stochastically assigned to ABC and BA sub-swarms in each iteration according to their fitness values. Consequently, the current healthier sub-swarm gains more agents to carry out its actions. Sub-swarm populations therefore vary ceaselessly during iterations, while either the ABC or BA sub-swarms may be superior to the other. Experiments are conducted on 23 benchmark functions. Results demonstrate that the TB performs better than or close to the ABC or BA winner. TB overcomes the poor performance of ABC and BA in handling particular problems.

关键词： Optimization Swarm intelligence Artificial bee colony bees algorithm Two bees algorithm

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Multi-objective optimal backstepping controller design for chaos control in a rod-type plasma torch system using bees algorithm

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APPLIED MATHEMATICAL MODELLING 2015年第15期39卷 4432-4444页

作者： Gholipour, Reza Khosravi, Alireza Mojallali, Hamed Babol Noushirvani Univ Technol Fac Elect & Comp Engn Babol Sar Iran Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

In this study, we propose an optimal backstepping controller for chaos control in a rod-type plasma torch system. The bees algorithm is used to determine the optimal parameters for the backstepping controller by minimizing a multi-objective function. The objective function is a weighted sum of the integral of time multiplied absolute error and the squared control signal. Our simulation results demonstrate the greater effectiveness of the proposed controller for chaos elimination in a rod-type plasma torch system compared with a sliding mode controller and a backstepping controller tuned by particle swarm optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Backstepping controller bees algorithm Control of chaos Rod-type plasma torch system

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An improved ensemble pruning for mammogram classification using modified bees algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第12期34卷 10093-10116页

作者： Qasem, Ashwaq Abdullah, Siti Norul Huda Sheikh Sahran, Shahnorbanun Albashish, Dheeb Goudarzi, Shidrokh Arasaratnam, Shantini Univ Kebangsaan Malaysia Fac Informat Sci & Technol Bangi 43600 Selangor Malaysia Taiz Univ Fac Engn & Informat Technol Taizi Yemen Al Balqa Appl Univ Comp Sci Dept Prince Abdullah Bin Ghazi Fac Informat & Commun T Salt Jordan Gen Hosp Kuala Lumpur Dept Radiol Kuala Lumpur Malaysia

Ensemble learning has piqued the curiosity of the machine learning applications. It recently drawn serious attention in computer-aided diagnostic system (CADs) due to their potential to significantly increase the prediction performance, especially in mass classification in the mammogram. The ensemble pruning technique is used to reduce the ensemble size and improve its performance by selecting an optimal subset from a pool of individual classifiers. Among ensemble pruning techniques, the metaheuristic bees algorithm (BA) showed reliable performance in terms of the selected ensemble's accuracy. However, BA's random initialization cannot guarantee whether the optimal ensemble will be selected, which leads to lesser classification results. Thus, this study introduces a selective Random Start Best step (RSB) initialization for BA to get an optimal ensemble pruning solution. Moreover, fusing ensemble members with equal weights will reduce the performance of the ensemble. To overcome this issue, a Local Weighted Majority Voting (L-WMV) is proposed beside the RSB. The proposed RSB(L-WMV) method for solving ensemble pruning and fussing issues was assessed on the mammogram image dataset that has been collected from Hospital Kuala Lumpur (HKL). Furthermore, the proposed RSB(L-WMV) was applied to the Mammographic Image Analysis Society (MIAS) benchmark dataset to show the proposed method's effectiveness. The obtained results using various evaluation metrics (accuracy, sensitivity, specificity, AUC) reveal the superiority of the proposed RSB(L-WMV) compared to similar methods in the literature.

关键词： bees algorithm Medical image processing Ensemble pruning Ensemble fusion

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A hybrid self-adaptive bees algorithm for examination timetabling problems

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APPLIED SOFT COMPUTING 2013年第8期13卷 3608-3620页

作者： Abdullah, Salwani Alzaqebah, Malek Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Selangor Malaysia

A hybrid self-adaptive bees algorithm is proposed for the examination timetabling problems. The bees algorithm (BA) is a population-based algorithm inspired by the way that honey bees forage for food. The algorithm presents a type of neighbourhood search that includes a random search that can be used for optimisation problems. In the BA, the bees search randomly for food sites and return back to the hive carrying the information about the food sites (fitness values);then, other bees will select the sites based on their information (more bees are recruited to the best sites) and will start a random search. We propose three techniques (i.e. disruptive, tournament and rank selection strategies) for selecting the sites, rather than using the fitness value, to improve the diversity of the population. Additionally, a self-adaptive strategy for directing the neighbourhood search is added to further enhance the local intensification capability. Finally, a modified bees algorithm is combined with a local search (i.e. simulated annealing, late acceptance hill climbing) to quickly descend to the optimum solution. Experimental results comparing our proposed modifications with each other and with the basic BA show that all of the modifications outperform the basic BA;an overall comparison has been made with the best known results on two examination timetabling benchmark datasets, which shows that our approach is competitive and works well across all of the problem instances. (C) 2013 Elsevier B. V. All rights reserved.

关键词： bees algorithm Examination timetabling problems Late acceptance hill climbing algorithm Selection strategy Self-adaptive mechanism Simulated annealing algorithm

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Printed circuit board assembly time minimisation using a novel bees algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年第0期133卷 186-194页

作者： Castellani, Marco Otri, Sameh Duc Truong Pham Univ Birmingham Dept Mech Engn Birmingham B15 2TT W Midlands England Cardiff Univ Sch Engn Cardiff CF24 3AA S Glam Wales

This paper presents a novel version of the bees algorithm customised to solve combinatorial optimisation problems. This version was created to minimise assembly time in the manufacturing of printed circuit boards using a machine of the moving-board-with-time-delay type, and optimising the feeder arrangement and machine component placement sequence. The local search procedure of the standard bees algorithm was modified to include five new operators for combinatorial optimisation. The customised bees algorithm was first tested on the related travelling salesman problem, where it excelled in terms of performance and efficiency compared to three state-of-the-art optimisation methods. It was then applied to a well-known moving-board-with-time-delay benchmark problem, where it performed favourably in comparison to the state-of-the-art in the literature, achieving fast and consistent solutions.

关键词： Combinatorial optimisation bees algorithm Printed circuit board assembly Manufacturing

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Fuzzy Self-tuning bees algorithm for designing optimal product lines

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Zervoudakis, Konstantinos Tsafarakis, Stelios Tech Univ Crete Sch Prod Engn & Management Khania Greece

The Product Line Design (PLD) problem is an NP-hard combinatorial optimization problem in marketing that aims at determining an optimal product line through which a firm can optimize a desired objective, like its profits or market share. Since the PLD problem has been proved to have high complexity in real-life applications, high-quality solutions have been detected by researchers who develop various optimization methods and test their performance. The bees algorithm (BA) is a successful swarm intelligent optimization algorithm which is based on the behavior of bees. The aim of this research is to develop and assess BA in the optimal PLD problem. In this effort, a set of fuzzy rules has been developed to autonomously compute parameters for each individual solution throughout the optimization process. The performance of two BA variants is compared with those of popular previous approaches, using both real and simulated data of customer preferences. The findings reveal that BA constitutes an enhanced alternative approach for designing optimal product lines.

关键词： Combinatorial optimization Product line design bees algorithm NP-hard Marketing

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Finite element model updating using bees algorithm

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2010年第2期42卷 283-291页

作者： Moradi, Shapour Fatahi, Laleh Razi, Pejman Shahid Chamran Univ Dept Mech Engn Ahvaz 61355 Iran

In this paper the application of bees algorithm (BA) in the finite element (FE) model updating of structures is investigated. BA is an optimization algorithm inspired by the natural foraging behavior of honeybees to find food sources. The weighted sum of the squared error between the measured and computed modal parameters is used as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. To this end, the modal parameters of the numerical model are compared with the experimental ones obtained through modal testing. Moreover, to verify the performance of BA, it is compared with the genetic algorithm, the particle swarm optimization and the inverse eigensensitivity method. Comparison of the results indicates that BA is a simple and robust approach that could be effectively applied to the FE model updating problems.

关键词： bees algorithm Finite element model updating Genetic algorithm Inverse Eigensensitivity method Particle swarm optimization Piping system

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