scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

Design optimization of rotary regenerator using artificial bee colony algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1088-1098 ◽  
Author(s):  
R V Rao ◽  
V Patel
Keyword(s):  
Design Optimization ◽  
Artificial Bee Colony ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Design Variables ◽  
Fitness Value ◽  
Single Objective

This study explores the use of artificial bee colony (ABC) algorithm for the design optimization of rotary regenerator. Maximization of regenerator effectiveness and minimization of regenerator pressure drop are considered as objective functions and are treated individually and then simultaneously for single-objective and multi-objective optimization, respectively. Seven design variables such as regenerator frontal area, matrix rotational speed, matrix rod diameter, matrix thickness, porosity, and split are considered for optimization. A case study is also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using ABC algorithm are validated by comparing with those obtained using genetic algorithm for the same case study. The effect of variation of ABC algorithm parameters on convergence and fitness value of the objective function has also been presented.

scholarly journals Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chao Yang ◽  
Jian-Ke Zhang ◽  
Li-Xin Guo
Keyword(s):  
Convergence Rate ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Research Field ◽  
Invasive Weed ◽  
Local Optima ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct

The artificial bee colony (ABC) algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL) and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC) technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO) and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

scholarly journals Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

2020 ◽  
Vol 41 (1) ◽  
pp. 34-49
Author(s):  
Sandip B. Gunjal ◽  
Padmakar J. Pawar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Bee Colony ◽  
Abrasive Finishing

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario.

scholarly journals A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

2019 ◽  
Vol 11 (24) ◽  
pp. 6969 ◽  
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Optimization Model ◽  
Optimization Method ◽  
High Energy ◽  
Line Balancing ◽  
Multi Objective Optimization ◽  
Automotive Engine ◽  
Multi Objective ◽  
Proposed Model ◽  
Disassembly Line Balancing ◽  
Disassembly Line

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.

Artificial bee colony algorithm for solving multi-objective distributed fuzzy permutation flow shop problem

2021 ◽  
pp. 1-11
Author(s):  
M. Emin Baysal ◽  
Ahmet Sarucan ◽  
Kadir Büyüközkan ◽  
Orhan Engin
Keyword(s):  
Artificial Bee Colony ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Abc Algorithm ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Bee Colony ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems.

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