PERFORMANCE COMPARISON OF DIFFERENTIAL EVOLUTION AND SOMA ON CHAOS CONTROL OPTIMIZATION PROBLEMS

2012 ◽  
Vol 22 (08) ◽  
pp. 1230025 ◽  
Author(s):  
ROMAN SENKERIK ◽  
DONALD DAVENDRA ◽  
IVAN ZELINKA ◽  
ZUZANA OPLATKOVA ◽  
ROMAN JASEK
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Chaos Control ◽  
Optimization Problems ◽  
Performance Comparison ◽  
Quality Of Results ◽  
Control Optimization ◽  
Extreme Sensitivity ◽  
Small Change

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.

scholarly journals Improving the Performance of Metaheuristics: An Approach Combining Response Surface Methodology and Racing Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Eduardo Batista de Moraes Barbosa ◽  
Edson Luiz França Senne ◽  
Messias Borges Silva
Keyword(s):  
Optimization Problems ◽  
Great Influence ◽  
Solution Process ◽  
Fine Tuning ◽  
Quality Of Results ◽  
Process Understanding ◽  
Tuning Process ◽  
Research Challenge ◽  
Best Fit

The setup of heuristics and metaheuristics, that is, the fine-tuning of their parameters, exercises a great influence in both the solution process, and in the quality of results of optimization problems. The search for the best fit of these algorithms is an important task and a major research challenge in the field of metaheuristics. The fine-tuning process requires a robust statistical approach, in order to aid in the process understanding and also in the effective settings, as well as an efficient algorithm which can summarize the search process. This paper aims to present an approach combining design of experiments (DOE) techniques and racing algorithms to improve the performance of different algorithms to solve classical optimization problems. The results comparison considering the default metaheuristics and ones using the settings suggested by the fine-tuning procedure will be presented. Broadly, the statistical results suggest that the fine-tuning process improves the quality of solutions for different instances of the studied problems. Therefore, by means of this study it can be concluded that the use of DOE techniques combined with racing algorithms may be a promising and powerful tool to assist in the investigation, and in the fine-tuning of different algorithms. However, additional studies must be conducted to verify the effectiveness of the proposed methodology.

Multiobjective Differential Evolution Based on Fuzzy Performance Feedback

2014 ◽  
Vol 5 (4) ◽  
pp. 45-64 ◽  
Author(s):  
Chatkaew Jariyatantiwait ◽  
Gary G. Yen
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Performance Feedback ◽  
Performance Metrics ◽  
Fuzzy Inference ◽  
Optimization Problems ◽  
Evolution Process ◽  
Exploration And Exploitation ◽  
Multiobjective Optimization Problems ◽  
Mutation Strategy

Differential evolution is often regarded as one of the most efficient evolutionary algorithms to tackle multiobjective optimization problems. The key to success of any multiobjective evolutionary algorithms (MOEAs) is maintaining a delicate balance between exploration and exploitation throughout the evolution process. In this paper, the authors propose a Fuzzy-based Multiobjective Differential Evolution (FMDE) that uses performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. The authors apply the fuzzy inference rules to these metrics in order to dynamically adjust the associated control parameters of a chosen mutation strategy used in this algorithm. One parameter controls the degree of greedy or exploitation, while another regulates the degree of diversity or exploration of the reproduction phase. Therefore, the authors can appropriately adjust the degree of exploration and exploitation through performance feedback. The performance of FMDE is evaluated on well-known ZDT and DTLZ test suites. The results validate that the proposed algorithm is competitive with respect to chosen state-of-the-art MOEAs.

scholarly journals Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1906
Author(s):  
Amarjeet Prajapati ◽  
Zong Woo Geem
Keyword(s):  
Evolutionary Algorithms ◽  
Software Architecture ◽  
Architectural Design ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Performance Criteria ◽  
Software System ◽  
Multi Objective

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.

scholarly journals A Phase-Based Adaptive Differential Evolution Algorithm for the Economic Load Dispatch Considering Valve-Point Effects and Transmission Losses

2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Xin Shen ◽  
Dexuan Zou ◽  
Xin Zhang ◽  
Qiang Zhang ◽  
Peng Xiao
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Performance Comparison ◽  
Test Cases ◽  
Economic Load Dispatch ◽  
Transmission Losses ◽  
Adaptive Differential Evolution ◽  
Feasible Solutions ◽  
Operating Constraints

A phase-based adaptive differential evolution (PADE) algorithm is proposed to solve the economic load dispatch (ELD) considering valve-point effects (VPE) and transmission losses. To a great extent, PADE makes up for the drawbacks of the traditional differential evolution (DE) through three improvements. First, we establish an archive of storing successful individuals to improve the quality of offspring. Second, to balance the exploring and exploiting ability of the algorithm, a phase-based mutation operation is carried out. Third, two control parameters are adaptively adjusted, which is helpful for enhancing the robustness of the algorithm. In addition, two types of repair methods of constraint handling are employed for the ELD without or with transmission losses to help PADE find feasible solutions more efficiently. A performance comparison between PADE and other DE approaches from the literature was carried out on six ELD test cases which consider a set of operating constraints including the VPE and transmission losses. Results show a competitive PADE performance in all test cases regarding the compared DE approaches. Compared to methods from the literature, the costs obtained by PADE are lower in most cases while the corresponding constraint violations reach a lower level.

An Improved Differential Evolution and its Application in Function Optimization Problem

2011 ◽  
Vol 267 ◽  
pp. 632-634
Author(s):  
Jing Feng Yan ◽  
Chao Feng Guo
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Search Strategy ◽  
Optimization Problems ◽  
Function Optimization ◽  
Final Solution ◽  
De Algorithm ◽  
Ranking Strategy ◽  
The Stability

An Improved Differential evolution (IDE) is proposed in this paper. It has some new features: 1) using multi-parent search strategy and stochastic ranking strategy to maintain the diversity of the population; 2) a novel convex mutation to accelerate the convergence rate of the classical DE algorithm.; The algorithm of this paper is tested on 13 benchmark optimization problems with linear or/and nonlinear constraints and compared with other evolutionary algorithms. The experimental results demonstrate that the performance of IDE outperforms DE in terms of the quality of the final solution and the stability.

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