Utilizing the Modified Self-Adaptive Differential Evolution Algorithm in Dynamic Cellular Manufacturing System

2012 ◽  
Vol 3 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Mohammad Hassannezhad ◽  
Nikbakhsh Javadian
Keyword(s):  
Differential Evolution ◽  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Computational Time ◽  
Test Problems ◽  
Line Production ◽  
Adaptive Differential Evolution ◽  
Self Adaptive

Today, Cellular Manufacturing Systems (CMS) have been introduced as a mixture of work-shop manufacturing and line-production systems for keeping efficiency and flexibility synchronously. One of the difficult steps of designing CMS is the Cell Formation (CF) problem in which parts with similar processes are made in one cell. Solving a dynamic integer model of CF with three sub-objective functions is considered using evolutionary algorithms. Due to the fact that CF is a NP-hard problem, solving the model using classical optimization methods needs long computational time. In this paper, a nonlinear integer model of CF is presented and then solved by proposed Modified Self-adaptive Differential Evolution (MSDE) and Modified Genetic Algorithm (MGA) using a set of 25 test problems. The results are compared with the optimal solution, and the efficiency of MSDE algorithm is discussed.

scholarly journals OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 141 ◽  
Author(s):  
Cemre Cubukcuoglu ◽  
Berk Ekici ◽  
Mehmet Fatih Tasgetiren ◽  
Sevil Sariyildiz
Keyword(s):  
Differential Evolution ◽  
Architectural Design ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Modular System ◽  
Test Problems ◽  
Design Problems ◽  
Adaptive Differential Evolution ◽  
Mutation Strategies ◽  
Self Adaptive

Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system.

Design of a Parallel Manipulator Based on Multi-Objective Self-Adaptive Differential Evolution Algorithm

2013 ◽  
Vol 328 ◽  
pp. 3-8 ◽  
Author(s):  
Qing Mei Meng
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Search Space ◽  
Parallel Robot ◽  
Feasible Region ◽  
Multi Objective ◽  
Trial Vector ◽  
Adaptive Differential Evolution ◽  
Optimal Pid Control ◽  
Self Adaptive

In order to improve highly non-isotropic input-output relations in the optimal design of a parallel robot, this paper presents a method based on a multi-objective self-adaptive differential evolution (MOSaDE) algorithm.The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. In theMOSaDE algorithm, both trial vector generation strategies and their associated control parameter values were gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings could be determined adaptively to match different phases of the search processevolution.Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The empirical analysis of thenumerical results shows the efficiency of the proposed algorithm.

scholarly journals Nested bi-level metaheuristic algorithms for cellular manufacturing systems considering workers’ interest

2019 ◽  
Author(s):  
Bardia Behnia ◽  
Babak Shirazi ◽  
Iraj Mahdavi ◽  
Mohammad Mahdi Paydar
Keyword(s):  
Manufacturing Systems ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Life Cycles ◽  
Strategic Decision ◽  
Computational Time ◽  
Test Problems ◽  
Special Role ◽  
Cellular Manufacturing Systems ◽  
Level Model

Due to the competitive nature of the market and the various products production requirements with short life cycles, cellular manufacturing systems have found a special role in manufacturing environments. Creativity and innovation in products are the results of the mental effort of the workforces in addition to machinery and parts allocation. Assignment of the workforce to cells based on the interest and ability indices is a tactical decision while the cell formation is a strategic decision. To make the correct decision, these two problems should be solved separately while considering their impacts on each other classically. For this reason, a novel bi-level model is designed to make decentralized decisions. Because of the importance of minimizing voids and exceptional element in the cellular manufacturing system, it is considered as a leader at the first level and the assignment of human resources is considered as a follower at the second level. To achieve product innovation and synergy among staff in the objective function at the second level, increasing the worker’s interest in order to cooperate with each other is considered too. Given the NP-Hard nature of cell formation and bi-level programming, nested bi-level genetic algorithm and particle swarm optimization are developed to solve the mathematical model. Various test problems have been solved by applying these two methods and validated results have been shown the efficiency of the proposed model. Also, real experimental comparisons have been presented. These results in contrast with previous works have been shown the minimum amount of computational time, cell load variation, total intercellular movements, and total intracellular movements of this new method. These effects have an important role in order to the improvement of cellular manufacturing behavior.

Sign in / Sign up

Export Citation Format

Share Document