scholarly journals Enhanced Differential Evolution Algorithm with Local Search Based on Hadamard Matrix

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Changshou Deng ◽  
Xiaogang Dong ◽  
Yucheng Tan ◽  
Hu Peng
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Hadamard Matrix ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Test Problems ◽  
Search Performance ◽  
Local Space ◽  
Overall Performance

Differential evolution (DE) is a robust algorithm of global optimization which has been used for solving many of the real-world applications since it was proposed. However, binomial crossover does not allow for a sufficiently effective search in local space. DE’s local search performance is therefore relatively poor. In particular, DE is applied to solve the complex optimization problem. In this case, inefficiency in local research seriously limits its overall performance. To overcome this disadvantage, this paper introduces a new local search scheme based on Hadamard matrix (HLS). The HLS improves the probability of finding the optimal solution through producing multiple offspring in the local space built by the target individual and its descendants. The HLS has been implemented in four classical DE algorithms and jDE, a variant of DE. The experiments are carried out on a set of widely used benchmark functions. For 20 benchmark problems, the four DE schemes using HLS have better results than the corresponding DE schemes, accounting for 80%, 75%, 65%, and 65% respectively. Also, the performance of jDE with HLS is better than that of jDE on 50% test problems. The experimental results and statistical analysis have revealed that HLS could effectively improve the overall performance of DE and jDE.

scholarly journals A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-15
Author(s):  
Yalin Wang ◽  
Xiaofang Chen ◽  
Weihua Gui ◽  
Chunhua Yang ◽  
Lou Caccetta ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Pareto Frontier ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Production Quality ◽  
Benchmark Problems ◽  
De Algorithm ◽  
Searching Strategy ◽  
Grinding Machine

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.

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 Chaotic Search Based Equilibrium Optimizer for Dealing with Nonlinear Programming and Petrochemical Application

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 200
Author(s):  
Abd Allah A. Mousa ◽  
Mohammed A. El-Shorbagy ◽  
Ibrahim Mustafa ◽  
Hammad Alotaibi
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Phase I ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Limited Area ◽  
Search Performance ◽  
Specific Chemical ◽  
Chaotic Search

In this article, chaotic search based constrained equilibrium optimizer algorithm (CS-CEOA) is suggested by integrating a novel heuristic approach called equilibrium optimizer with a chaos theory-based local search algorithm for solving general non-linear programming. CS-CEOA is consists of two phases, the first one (phase I) aims to detect an approximate solution, avoiding being stuck in local minima. In phase II, the chaos-based search algorithm improves local search performance to obtain the best optimal solution. For every infeasible solution, repair function is implemented in a way such that, a new feasible solution is created on the line segment defined by a feasible reference point and the infeasible solution itself. Due to the fast globally converging of evolutionary algorithms and the chaotic search’s exhaustive search, CS-CEOA could locate the true optimal solution by applying an exhaustive local search for a limited area defined from Phase I. The efficiency of CS-CEOA is studied over multi-suites of benchmark problems including constrained, unconstrained, CEC’05 problems, and an application of blending four ingredients, three feed streams, one tank, and two products to create some certain products with specific chemical properties, also to satisfy the target costs. The results were compared with the standard evolutionary algorithms as PSO and GA, and many hybrid algorithms in the same simulation environment to approve its superiority of detecting the optimal solution over selected counterparts.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

scholarly journals Differential Evolution with Linear Bias Reduction in Parameter Adaptation

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 283
Author(s):  
Vladimir Stanovov ◽  
Shakhnaz Akhmedova ◽  
Eugene Semenkin
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Bias Reduction ◽  
Benchmark Problems ◽  
Basic Algorithm ◽  
Optimization Stage ◽  
Speed Up ◽  
Comparison Of The Results ◽  
Lehmer Mean

In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to improve the exploration properties at the beginning of the search and speed up the exploitation at the end of the search. As a basic algorithm, the L-SHADE approach is considered, as well as its modifications, namely the jSO and DISH algorithms. The experiments are performed on the CEC 2017 and 2020 bound-constrained benchmark problems, and the performed statistical comparison of the results demonstrates that the linear bias reduction allows significant improvement of the differential evolution performance for various types of optimization problems.

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Ali Khater Mohamed ◽  
Ehab Z. Elfeky ◽  
Mohamed Saleh
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Test Problems ◽  
New Mutation ◽  
De Algorithm ◽  
Engineering Problems ◽  
Evolution Algorithm ◽  
Mutation Scheme ◽  
Exploration Exploitation

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

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