The Hybrid Differential Evolution with Dynamic Scaling Mutation and Wrapper Local Search for Optimization Problems

2013 ◽  
Vol 479-480 ◽  
pp. 989-995
Author(s):  
Chun Liang Lu ◽  
Shih Yuan Chiu ◽  
Chih Hsu Hsu ◽  
Shi Jim Yen
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Search Space ◽  
Dynamic Scaling ◽  
Multi Objective ◽  
Wide Range ◽  
Single Objective

In this paper, an improved hybrid Differential Evolution (DE) is proposed to enhance optimization performance by cooperating Dynamic Scaling Mutation (DSM) and Wrapper Local Search (WLS) schemes. When evolution speed is standstill, DSM can improve searching ability to achieve better balance between exploitation and exploration in the search space. Furthermore, WLS can disturb individuals to fine tune the searching range around and then properly find better solutions in the evolution progress. The effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is also applied to always produce feasible candidate solutions for hybrid DE model to solve the Flexible Job-Shop Scheduling Problem (FJSP). To test the performance of the proposed hybrid method, the experiments contain five frequently used CEC 2005 numerical functions and three representative FJSP benchmarks for single-objective and multi-objective optimization verifications, respectively. Compare the proposed method with the other related published algorithms, the simulation results indicate that our proposed method exhibits better performance for solving most the test functions for single-objective problems. In addition, the wide range of Pareto-optimal solutions and the more Gantt chart diversities can be obtained for the multi-objective FJSP in practical decision-making considerations.

Using the Memetic Algorithm for Multi Objective Job Shop Scheduling Problems

2012 ◽  
Vol 544 ◽  
pp. 245-250 ◽  
Author(s):  
Guo Hui Zhang
Keyword(s):  
Search Strategy ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Scheduling Problems ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Multi Objective

The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

Multi-objective decision making and search space for the evaluation of production process scheduling

2009 ◽  
Vol 57 (3) ◽  
pp. 195-208 ◽  
Author(s):  
T. Witkowski ◽  
P. Antczak ◽  
A. Antczak
Keyword(s):  
Decision Making ◽  
Production Process ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Process Scheduling ◽  
Shop Scheduling ◽  
Multi Objective

Multi-objective decision making and search space for the evaluation of production process schedulingOver the years, various approaches have been proposed in order to solve the multi-objective job-shop scheduling problem - particularly a hard combinatorial optimization problem. The paper presents an evaluation of job shop scheduling problem under multiple objectives (mean flow time, max lateness, mean tardiness, mean weighted tardiness, mean earliness, mean weighted earliness, number of tardy tasks). The formulation of the scheduling problem has been presented as well as the evaluation schedules for various optimality criteria. The paper describes the basic mataheuristics used for optimization schedules and the approaches that use domination method, fuzzy method, and analytic hierarchy proccess (AHP) for comparing schedules in accordance with multiple objectives. The effectiveness of the algorithms has been tested on several examples and the results have been shown. New search space for evaluation and generation of schedules has been created. The three-dimensional space can be used for the analysis and control of the production processes.

scholarly journals Genetic Programming Hyper-heuristics for Job Shop Scheduling

2021 ◽  
Author(s):  
◽  
Rachel Hunt
Keyword(s):  
Genetic Programming ◽  
Local Search ◽  
Objective Function ◽  
Real World ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Heuristic Approach ◽  
Shop Scheduling ◽  
The Mean

<p>Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly.  The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability.  Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used.  Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness.   Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved.  Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs.  Through this thesis the following major contributions have been made: (1) the first use of GP to evolve optimal DRs for the static two-machine job shop with makespan objective function; (2) an approach to developing less-myopic DRs through the inclusion of wider looking terminals and the use of local search to provide additional fitness information over an extended decision horizon; (3) the first use of STGP for the automatic discovery of DRs with better interpretability and semantic validity for increased trust; and (4) the first multiobjective GP approach that considers multiple objectives investigating the trade-off between scheduling behaviour and interpretability. This is also the first work that uses analysis of evolved GP individuals to perform feature selection and construction for JSS.</p>

srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier

2021 ◽  
pp. 1-15
Author(s):  
Anh Tran ◽  
Michael S. Eldred ◽  
Scott McCann ◽  
Yan Wang
Keyword(s):  
Latent Variable ◽  
Flip Chip ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Element Model ◽  
Bayesian Optimization ◽  
Multi Objective ◽  
The Third ◽  
Wide Range ◽  
Single Objective

Abstract Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective BO formalism, called srMO-BO-3GP, to solve multi-objective optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GPs is assigned with a different task. The first GP is used to approximate a single-objective computed from the multi-objective definition, the second GP is used to learn the unknown constraints, and the third one is used to learn the uncertain Pareto frontier. At each iteration, a multi-objective augmented Tchebycheff function is adopted to convert multi-objective to single-objective, where the regularization with a regularized ridge term is also introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the convergence and diversity of the Pareto frontier by the acquisition function for exploitation and exploration. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

scholarly journals Genetic Programming Hyper-heuristics for Job Shop Scheduling

2021 ◽  
Author(s):  
◽  
Rachel Hunt
Keyword(s):  
Genetic Programming ◽  
Local Search ◽  
Objective Function ◽  
Real World ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Heuristic Approach ◽  
Shop Scheduling ◽  
The Mean

<p>Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly.  The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability.  Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used.  Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness.   Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved.  Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs.  Through this thesis the following major contributions have been made: (1) the first use of GP to evolve optimal DRs for the static two-machine job shop with makespan objective function; (2) an approach to developing less-myopic DRs through the inclusion of wider looking terminals and the use of local search to provide additional fitness information over an extended decision horizon; (3) the first use of STGP for the automatic discovery of DRs with better interpretability and semantic validity for increased trust; and (4) the first multiobjective GP approach that considers multiple objectives investigating the trade-off between scheduling behaviour and interpretability. This is also the first work that uses analysis of evolved GP individuals to perform feature selection and construction for JSS.</p>

Localization for Solving Noisy Multi-Objective Optimization Problems

2009 ◽  
Vol 17 (3) ◽  
pp. 379-409 ◽  
Author(s):  
Lam T. Bui ◽  
Hussein A. Abbass ◽  
Daryl Essam
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Local Models ◽  
Multi Objective Optimization ◽  
Global Models ◽  
Average Performance ◽  
Multi Objective ◽  
Wide Range ◽  
Filter Noise

This paper investigates the use of a framework of local models in the context of noisy evolutionary multi-objective optimization. Within this framework, the search space is explicitly divided into several nonoverlapping hyperspheres. A direction of improvement, which is related to the average performance of the spheres, is used for moving solutions within each sphere. This helps the local models to filter noise and increase the robustness of the evolutionary algorithm in the presence of noise. A wide range of noisy problems we used for testing and the experimental results demonstrate the ability of local models to better filter noise in comparison with that of global models.

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