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A novel Fibonacci-inspired enhancement of the bees algorithm: application to robotic disassembly sequence planning

COGENT ENGINEERING 2024年第1期11卷

作者： Hartono, Natalia Pham, D. T. Univ Birmingham Mech Engn Dept Birmingham B15 2TT England Univ Pelita Harapan Ind Engn Dept Tangerang Indonesia

The bees algorithm (BA) is an intelligent, nature-inspired metaheuristic algorithm first introduced in 2005 and based on the foraging activity of honeybees. Enhancements to the BA have been made continually since its introduction, with some aimed at reducing the number of user-determined parameters. The goal is to achieve optimal results without extensive efforts required for parameter tuning. This article presents an enhanced version of the BA. The enhancement uses the Fibonacci sequence, which can be found in the family tree of the male bee (drone), to give the numbers of bees assigned to conduct local searches. The proposed BA is simple, having only four parameters that must be set by the user, as opposed to five in the basic combinatorial version. To evaluate the performance of the new algorithm in robotic disassembly sequence planning, a case study is conducted on gear pump disassembly, a commonly used benchmark problem in robotic disassembly. A comparison is made between the proposed Fibonacci bees algorithm and the Basic bees algorithm (BBA). The results are assessed using a statistical performance metric and a performance evaluation index. The findings demonstrate that the proposed algorithm is better than the BBA for more complex problems while exhibiting a similar performance on simpler tasks. Future research will explore broader applications for both continuous and combinatorial problems, demonstrating the versatility of the algorithm across different domains.

关键词： bees algorithm disassembly Fibonacci optimisation remanufacturing Zude Zhou, Wuhan University of Technology, China Operation Research Optimization Techniques Computational Techniques Design of Experiments General

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Improvement of scatter search using bees algorithm

Improvement of scatter search using Bees Algorithm

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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Ali Makki Sagheer Ahmed T. Sadiq Mohammed S. Ibrahim Computer Science Department College of Computer University of Anbar Iraq Computer Science Department University of Technology Baghdad Iraq

ISBN: (纸本)9781467323925

The Scatter Search is one of the population-based metaheuristics, which can be used to solve a wide range of problems including Traveling Salesman Problem (TSP), Routing Protocol, and End-to-End QoS, while bees algorithm can be applied to optimize the solution of these problems. In this paper, an improved Scatter Search algorithm based on bees algorithm has been presented. The improvement provides Scatter Search with random exploration for search space of problem and more of intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithm produces better performance than original Scatter Search algorithm. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

关键词： component Metaheuristic Scatter Search bees algorithm Combinatorial Problems Traveling Salesman Problem travelling salesman problems Scattering Searches Bee SEARCHING algorithm

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A comparative study of the bees algorithm as a tool for function optimisation

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COGENT ENGINEERING 2015年第1期2卷

作者： Pham, Duc Truong Castellani, Marco Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

The bees algorithm is a parameter optimisation method that mimics the foraging behaviour of honey bees. This paper presents an experimental study of the performance of the bees algorithm. Its strengths and weaknesses are analysed, and the most suitable parameterizations in relation to different optimisation tasks are revealed. The robustness of the optimisation results to reasonable modifications of the fitness landscape is studied for a large set of parameterizations. The bees algorithm is tested on 18 custom-made function minimisation benchmarks, and its performance compared to that of two state-of-the-art biologically inspired optimisation methods. Thanks to their two-dimensional nature, the proposed fitness landscapes are easy to visualise. Experimental evidence indicates also that they constitute a varied and challenging set of test cases, useful to reveal the specific abilities and biases of the search algorithms. In addition, the performance of the bees algorithm and the other two optimisation methods is tested on four real-world minimisation tasks from the literature. The results obtained on the benchmarks prove the effectiveness and robustness of the bees foraging metaphor, in particular on the most deceptive and high-dimensional fitness landscapes. They also confirm the ability of the bees algorithm to solve complex real-world optimisation tasks.

关键词： optimisation bees algorithm swarm intelligence evolutionary algorithms optimisation benchmarks variable neighbourhood search

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A user-friendly bees algorithm for continuous and combinatorial optimisation

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COGENT ENGINEERING 2023年第2期10卷

作者： Ismail, Asrul Harun Ruslan, Wegie Pham, Duc Truong Univ Portsmouth Ctr Operat Res & Logist Sch Math & Phys Portsmouth England Univ Birmingham Sch Engn Bees Algorithm Res Grp Birmingham England Univ Pancasila Dept Ind Engn Jakarta Indonesia Catholic Univ Indonesia Atma Jaya Dept Mech Engn Jakarta Indonesia

This paper introduces a new variant of the bees algorithm (BA) called bees algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems.

关键词： bees algorithm nature-inspired algorithm bee-inspired algorithm metaheuristics continuous optimisation problem combinatorial optimisation problem

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A Novel Combination of bees and Firefly algorithm to Optimize Continuous Problems 8

A Novel Combination of Bees and Firefly Algorithm to Optimiz...

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8th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Gholami, Jafar Mohammadi, Somayeh Islamic Azad Univ Kermanshah Branch Dept Comp Engn Kermanshah Iran Islamic Azad Univ Kermanshah Sci & Res Branch Dept Comp Engn Kermanshah Iran Islamic Azad Univ Ghasr E Shirin Branch Dept Comp Engn Ghasr E Shirin Iran

ISBN: (纸本)9781538695692

Although bees algorithm (BA) has presented great performance so far in optimizing different problems, it still has weak convergence rate in some optimizations. Therefore, in this investigation in order to resolve its weakness and boost the exploitation of algorithm, it was hybridized by Firefly algorithm (FA). The hybrid bees and firefly algorithm (BA-FA) was implemented on twelve numerical benchmark functions to evaluate it. The BA-FA was also compared with four famous algorithms, including particle swarm optimization (PSO), invasive weed optimization (IWO) and so on. The results showed that BA-FA had the best performance and convergence in optimizing the mentioned functions.

关键词： bees algorithm firefly algorithm hybrid bees algorithm and firefly (BA-FA) meta-heuristic algorithm

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Adjusted Case-Based Software Effort Estimation Using bees Optimization algorithm

Adjusted Case-Based Software Effort Estimation Using Bees Op...

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15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Azzeh, Mohammad Appl Sci Univ Fac Informat Technol Amman Jordan

ISBN: (纸本)9783642238628;9783642238635

Case-Based Reasoning (CBR) has achieved a considerable interest from researchers for solving non-trivial or ill-defined problems such as those encountered by project managers including support for software project management in predictions and lesson learned. Software effort estimation is the key factor for successful software project management. In particular, the use of CBR for effort estimation was favored over regression and other machine learning techniques due to its performance in generating reliable estimates. However, this method was subject to variety of design options which therefore has strong impact on the prediction accuracy. Selection of CBR adjustment method and deciding on the number of analogies are such two important decisions for generating accurate and reliable estimates. This paper proposed a new method to adjust the retrieved project efforts and find optimal number of analogies by using bees optimization algorithm. The bees algorithm will be used to search for the best number of analogies and features coefficient values that will be used to reduce estimates errors. Results obtained are promising and the proposed method could form a useful extension for Case-based effort prediction model.

关键词： Case-Based Reasoning Software Effort Estimation bees algorithm

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Optimal Location and Sizing of Solar Farm on Japan East Power System Using Multiobjective bees algorithm

Optimal Location and Sizing of Solar Farm on Japan East Powe...

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IEEE Energytech

作者： Prakomchai Phonrattanasak Masafumi Miyatake Orie Sakamoto Department of Electrical Engineering North Eastern University Department of Electrical Engineering Sophia University

ISBN: (纸本)9781467344456

Nowadays some nuclear power plant in Japan is shut down due to problem of security in earthquake disaster. Consequently, Japan will encounter a lack of electrical energy in the future if new electrical generation is not constructed. Solar energy is one of appropriate renewable energy for Japan. Solar panel produces electrical energy by using the natural solar radiation from sun. PV Solar farm consists of multiple components, including the photovoltaic modules, mechanical and electrical connections. PV solar farm with grid-connected can directly connect to the existing electricity grid by using inverter circuit toward transformer. In this paper, PV solar farm is utilized as a power generation which injects active power into a Japan east power system. An optimal location and sizing of solar farm by using multiobjective bees optimization (MBO) is proposed to minimize fuel and emission costs of overall system with considering solar radiation energy in each area. The results show that the proposed method found the optimal position of solar farm with minimum cost of fuel and environmental pollution.

关键词： Power system Optimization bees algorithm PV solar and Japan power system

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Updating Finite Element Model Using Stochastic Subspace Identification Method and bees Optimization algorithm

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2018年第2期15卷

作者： Alimouri, Pouyan Moradi, Shapour Chinipardaz, Rahim Shahid Chamran Univ Ahvaz Mech Engn Dept Ahvaz Iran Shahid Chamran Univ Ahvaz Stat Dept Ahvaz Iran

This study investigates the application of operational modal analysis along with bees optimization algorithm for updating the finite element model of structures. bees algorithm applies instinctive behavior of honeybees as they look for nectar of flowers. The parameters that needed to be updated are uncertain parameters such as geometry and material properties of the structure. To determine these uncertain parameters, local and global sensitivity analyses have been performed. An objective function is defined based on the sum of the squared errors between the natural frequencies obtained from operational modal analysis and finite element method. The natural frequencies of physical structure are determined by stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. To verify the accuracy of this method, the proposed algorithm is implemented on a three-story structure to update parameters of its finite element model. Moreover, to study the efficiency of bees algorithm, its results are compared with those of the particle swarm optimization, and Nelder and Mead methods. The comparison indicates that this algorithm leads more accurate results with faster convergence.

关键词： Vibration analysis Finite element model updating Operational modal analysis Stochastic subspace identification bees algorithm Sensitivity analysis

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Modelling and optimization of thermal sprayed Stellite-6 coating on steel substrate

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-18页

作者： Hajipour, Mohammad Mirshekari, Erfan Shahrooi, Shahram Islamic Azad Univ Dept Mech Engn Ahvaz Branch Ahvaz Iran

This study presents a simulation of the thermal spraying process of Stellite-6 microparticles onto a steel substrate using the finite element method. The investigation focuses on the effects of particle size, substrate pre-heating temperature, and spraying angle on key response variables such as stress, equivalent plastic strain (PEEQ), penetration depth, and temperature distribution at the particle-substrate interface. Among these, the penetration depth of particles significantly influences the bonding strength of the resulting coatings. Therefore, optimizing the process input parameters in order to maximize the penetration depth is essential for achieving high-quality coatings with strong adhesion to the substrates. To systematically design and conduct the simulations, the Design of Experiments (DOE) approach was employed. Subsequently, response surface methodology (RSM) was applied to examine the relationships between the input parameters and the resulting outputs. The simulation findings demonstrate a strong dependence of penetration depth on particle size, with the spraying angle also emerging as a critical factor. Specifically, an increase in particle size and spraying angle leads to greater penetration depth. Furthermore, process input parameters were optimized using the bees algorithm, with the established relationship for penetration depth serving as the objective function. The primary outcome of this optimization process was the maximization of penetration depth through the application of the bees algorithm. Optimization using the bees algorithm yielded a maximum penetration depth of 5.10E-03 mm corresponding to a particle size of 53 mu m, substrate temperature of 373 K, and spraying angle of 86.3 degrees, validating the efficiency of the proposed approach. The significance of this study lies in its systematic integration of advanced simulation and optimization techniques to enhance bonding strength in thermal spray coatings.

关键词： Thermal spraying Finite element Stellite-6 bees algorithm Response surface methodology

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A Comparative Study of Artificial Bee Colony, bees algorithms and Differential Evolution on Numerical Benchmark Problems

A Comparative Study of Artificial Bee Colony, Bees Algorithm...

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5th International Symposium on Intelligence Computation and Applications

作者： Li, Huanzhe Liu, Kunqi Li, Xia Shijiazhuang Univ Econ Dept Comp Sci Shijiazhuang 050031 Peoples R China

ISBN: (纸本)9783642163876

In this paper, performance of basic Artificial Bee Colony, bees and Differential evolution algorithms is compared on eight well-known benchmark problems. Most of experimental results show that the DE/best/1/exp scheme has the best performance on unimodal problems, bees algorithm has the second performance except Quadric and Rosenbrock functions. On multimodal problems bees algorithm has the best performance, and Artificial Bee Colony is the second.

关键词： Artificial bee colony algorithm bees algorithm Differential evolution Numerical optimization

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