Optimization is the process of finding the best possible solutions to a problem. It has been widely used in various areas especially in engineering problems. One of the common issues that is faced by some of the manuf...
详细信息
Optimization is the process of finding the best possible solutions to a problem. It has been widely used in various areas especially in engineering problems. One of the common issues that is faced by some of the manufacturers is finding drilling sequences of multiple holes. By drilling multiple holes with the least total path length, the manufacturer can save a lot of time and it can increase the productivity of the company. Thus, this study focuses on the drilling path of the multiple holes problem which has been solved by other researchers. This study uses the bees algorithm to find the best sequence of drilling holes (minimum total path length) and the results found are compared with the result of other algorithms. In addition to results comparison with other algorithms, the results obtained are verified with simulation results using MasterCAM software. The results comparison shows that the bees algorithm achieved comparable performance compared to other algorithms.
Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low co...
详细信息
ISBN:
(纸本)9781728185262
Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low cost. The ever-increasing consumption of energy makes it highly important to schedule tasks efficiently in CDCs. In addition, many factors in CDCs, e.g., the wind and solar energy and prices of power grid have spatial differences. It becomes a challenging problem of how to achieve the energy cost minimization for CDCs in such a market. This work applies a G/G/1 queuing system to evaluate the optimization of servers in each CDC. Furthermore, a single-objective constrained optimization problem is given and addressed by a proposed Simulated-annealing-based bees algorithm to yield a close-to-optimal solution. Based on it, a Fine-grained Task Scheduling (FTS) algorithm is designed to minimize the energy cost of CDCs by intelligently scheduling heterogeneous tasks among distributed CDCs. In addition, it also determines running speeds of servers and the number of switched-on servers in each CDC while strictly meeting tasks' delay bounds. Realistic data-driven results demonstrate that FTS outperforms its typical benchmark scheduling peers in terms of energy cost and throughput.
This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the bees algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight...
详细信息
This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the bees algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight and coverage-weight have been added to the BA to avoid any ambiguous situations during the prediction phase. The contributions of the proposed B4M algorithm are two-fold: the first novel contribution is in the field of swarm intelligence, using a new version of BA for automatic rule discovery, and the second novel contribution is the formulation of a weight metric based on quailty and coverage of the rules discovered from the dataset to carry out Meta-Pruning and making it suitable for any classification problem in the real world. The proposed algorithm was implemented and tested using five different datasets from University of California, at Irvine (UCI Machine Learning Repository) and was compared with other well-known classification algorithms. The results obtained using the proposed B4M show that it was capable of achieving better classification accuracy and at the same time reduce the number of rules in four out of five UCI datasets. Furthermore, the results show that it was not only effective and more robust, but also more efficient, making it at least as good as other methods such as C5.0, C4.5, Jrip and other evolutionary algorithms, and in some cases even better.
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 usin...
详细信息
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.
Automated disassembly of End-of-Life (EoL) products can be difficult to implement due to uncertainties in their conditions. An automatic re-planning function is required to enable flexible adjustments of disassembly p...
详细信息
Automated disassembly of End-of-Life (EoL) products can be difficult to implement due to uncertainties in their conditions. An automatic re-planning function is required to enable flexible adjustments of disassembly plans and thus increase disassembly efficiency. The re-planning function is able to detect subassemblies and separable components, and adjust disassembly sequences and directions when components interlock and are irremovable. This paper presents a two-pointer detection strategy to find detachable subassemblies very quickly. A summation operator and a list with two pointers are used to check the interferences between components in a minimum number of steps. Then, a ternary bees algorithm is proposed to identify new disassembly sequences and directions. The algorithm combines the merits of a greedy search and meta-heuristic techniques by using only three collaborative potential solutions and three concurrent operations. Experimental results show that the proposed approach is able to perform a rapid subassembly detection and sequence optimisation for a robotic disassembly task, thus allowing real-time re-planning.
In this article, a novel Permutation-based bees algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and p...
详细信息
In this article, a novel Permutation-based bees algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing bees algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.
The bees algorithm has been successfully applied for over a decade to a large number of optimisation problems. However, a mathematical analysis of its search capabilities, the effects of different parameters used, and...
详细信息
The bees algorithm has been successfully applied for over a decade to a large number of optimisation problems. However, a mathematical analysis of its search capabilities, the effects of different parameters used, and various design choices has not been carried out. As a consequence, optimisation of the bees algorithm has so far relied on trial-and-error experimentation. This paper formalises the bees algorithm in a rigorous mathematical description, beyond the qualitative biological metaphor. A review of the literature is presented, highlighting the main variants of the bees algorithm, and its analogies and differences compared with other optimisation methods. The local search procedure of the bees algorithm is analysed, and the results experimentally checked. The analysis shows that the progress of local search is mainly influenced by the size of the neighbourhood and the stagnation limit in the site abandonment procedure, rather than the number of recruited foragers. In particular, the analysis underlines the trade-off between the step size of local search (a large neighbourhood size favours quick progress) and the likelihood of stagnation (a small neighbourhood size prevents premature site abandonment). For the first time, the implications of the choice of neighbourhood shape on the character of the local search are clarified. The paper reveals that, particularly in high-dimensional spaces, hyperspherical neighbourhoods allow greater search intensification than hypercubic neighbourhoods. The theoretical results obtained in this paper are in good agreement with the findings of several experimental studies. It is hoped that the new mathematical formalism here introduced will foster further understanding and analysis of the bees algorithm, and that the theoretical results obtained will provide useful parameterisation guidelines for applied studies.
This paper carries out the nonlinear stability of nanocomposite multilayer organic solar cell (NMOSC) subjected to axial compressive loads. The model of organic solar cell is assumed to consist five layers: Al, P3HT:P...
详细信息
This paper carries out the nonlinear stability of nanocomposite multilayer organic solar cell (NMOSC) subjected to axial compressive loads. The model of organic solar cell is assumed to consist five layers: Al, P3HT:PCBM, PEDOT:PSS, IOT and Glass. Based on the classical plate theory, the basic equations are established taking into account the effect of elastic foundations and initial imperfection. The approximation solutions are selected based on the boundary conditions of the four edges of NMOSC. The equation which indicates the relationship between axial compressive loads and deflection amplitude of NMOSC is obtained by using the Galerkin method. bees algorithm is applied to maximize the value of critical buckling load with nine variables including the thickness of five layers, the length and the width of NMOSC and two stiffness coefficients of elastic foundations. The numerical results show the effect of geometrical and material parameters, initial imperfection and elastic foundations on the nonlinear static stability and the critical buckling load of NMOSC. Optimal values of nine geometrical parameters of NMOSC are also determined.
Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembl...
详细信息
Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.
In patients' healthcare, manual planning of operating rooms is very difficult due to having a large number of constraints that the planner should take into consideration. The aim of this paper is to solve the oper...
详细信息
ISBN:
(纸本)9783030011741;9783030011734
In patients' healthcare, manual planning of operating rooms is very difficult due to having a large number of constraints that the planner should take into consideration. The aim of this paper is to solve the operating room scheduling problem using a hybrid bees algorithm. Our focus is to solve a two-level variant of the problem, the Master Surgery Scheduling Problem and the Surgical Case Assignment Problem, where both hospital cost and patient cost are considered. We use a hybrid population based meta-heuristic, namely, a Hybrid BA with Simulated Annealing. The performance of our algorithm is compared against a Tabu Search single solution based method from the literature using the same test data. The experimental results demonstrate the advantages of using our proposed approach.
暂无评论