scholarly journals Research on Collaborative Optimization of Green Manufacturing in Semiconductor Wafer Distributed Heterogeneous Factory

2019 ◽  
Vol 9 (14) ◽  
pp. 2879 ◽  
Author(s):  
Jun Dong ◽  
Chunming Ye
Keyword(s):  
Production Scheduling ◽  
Learning Strategy ◽  
Green Manufacturing ◽  
Predatory Behavior ◽  
Collaborative Optimization ◽  
Initial Population ◽  
Local Optimum ◽  
Grey Wolf ◽  
Semiconductor Wafer ◽  
Nsga Ii

Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories’ and three factories’ test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II.

scholarly journals Research on Two-Stage Joint Optimization Problem of Green Manufacturing and Maintenance for Semiconductor Wafer

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Jun Dong ◽  
Chunming Ye
Keyword(s):  
Optimization Problem ◽  
Green Manufacturing ◽  
Total Carbon ◽  
Joint Optimization ◽  
Initial Population ◽  
Test Cases ◽  
Semiconductor Wafer ◽  
Two Stage ◽  
Np Hard Problem ◽  
Wafer Manufacturing

This paper proposes a two-stage joint optimization problem of green manufacturing and maintenance for semiconductor wafer (TSGMM-SW) considering manufacturing stage, inspection, and repair stage simultaneously, which is a typical NP-hard problem with practical research significance and value. Aiming at this problem, a green scheduling model with the objective of minimizing makespan, total carbon emissions, and total preventive maintenance (PM) costs is constructed, and an improved hybrid multiobjective multiverse optimization (IHMMVO) algorithm is proposed in this paper. The joint optimization of green manufacturing and maintenance is realized by designing synchronous scheduling and maintenance strategy for wafer manufacturing and equipment PM. The diversity of the population is expanded and the optimization performance of IHMMVO is improved by designing the initial population fusion strategy and subpopulation evolution strategy. In the experimental phase, we perform the simulation experiments of 900 test cases randomly generated from 90 parameter combinations. The IHMMVO algorithm is compared with other existing algorithms to verify the effectiveness and feasibility for TSGMM-SW.

scholarly journals A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 291
Author(s):  
Wenbiao Yang ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Min Xie ◽  
Fei Song
Keyword(s):  
Extreme Learning Machine ◽  
Learning Strategy ◽  
Classification Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Control Factor ◽  
Initial Population ◽  
Local Optimum ◽  
Marine Predator ◽  
Learning Machine

A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

scholarly journals Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Qinghua Gu ◽  
Xuexian Li ◽  
Song Jiang
Keyword(s):  
Global Optimization ◽  
Large Scale ◽  
Learning Strategy ◽  
Optimization Problems ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Genetic Operators ◽  
Opposition Based Learning ◽  
Grey Wolf Algorithm

Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.

scholarly journals Optimization on Vehicle Routing Problem Based on Immune Clonal Selection Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 256-260
Author(s):  
Hai Hua Li ◽  
Zong Yan Xu ◽  
Fei Fei Zhou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clonal Selection ◽  
Solution Space ◽  
Genetic Mutation ◽  
Initial Population ◽  
Local Optimum ◽  
Clonal Selection Algorithm ◽  
Routing Problem ◽  
Optimum Solution

Vehicle routing problem is a typical NP-hard problem and is difficult to get an optimum solution. Aiming at the shortages of the existing methods, this paper proposed an algorithm based on immune clonal selection to solve vehicle routing problem. In the algorithm, expressed antibody with matrix, generated the initial population of antibodies randomly, and employed the operations such as clonal selection, genetic mutation iteratively to search optimum solution in solution space. The experimental results show that the algorithm presented here can converge to the global optimum solution rapidly, overcoming such disadvantages of the genetic algorithm as slower convergent velocity and the convergence to a local optimum solution.

Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

2022 ◽  
pp. 1-12
Author(s):  
Yang Li ◽  
Simeng Chen ◽  
Ke Bai ◽  
Hao Wang
Keyword(s):  
Path Planning ◽  
Electrostatic Discharge ◽  
Fire Risk ◽  
Convergence Time ◽  
Economic Losses ◽  
Risk Level ◽  
Support Vector ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

scholarly journals Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhao ◽  
Zhicheng Jia ◽  
Lei Chen ◽  
Yanju Guo
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Population Control ◽  
Pso Algorithm ◽  
Convergence Speed ◽  
Evolutionary Equation ◽  
Control Factor ◽  
Local Optimum ◽  
Backtracking Search ◽  
Backtracking Search Algorithm

Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.

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