Differential Evolution versus Genetic Algorithms in Multiobjective Optimization

Tušar, Tea; Filipič, Bogdan

doi:10.1007/978-3-540-70928-2_22

Cited by 103 publications

(72 citation statements)

References 16 publications

(22 reference statements)

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“…DE outperforms the above-mentioned prominent algorithms in terms of least number of generations for finding global minimum [15]. Similar results are reported in the following studies [16][17][18]. Hegery et al [16] carried out comparisons between DE and GA on N-Queen and travelling salesman problem and concluded that the performance of DE is better.…”

Section: Introductionsupporting

confidence: 69%

“…Hegery et al [16] carried out comparisons between DE and GA on N-Queen and travelling salesman problem and concluded that the performance of DE is better. Tušar and Filipič [17] carried out comparisons between DE-based variants DEMO and basic GA on multiobjective optimization problem and their result showed that DEMO outperforms basic GA. Vesterstrøm and Thomsen [18] noted that DE outperforms PSO and Evolutionary Algorithms (EAs) on majority of numerical benchmark problems. DE consists of population size (NP), scaling factor ( ), and crossover rate (CR) which significantly affect the performance of DE [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

Fung

Jing

2014

ISRN Robotics

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This paper proposed a method to improve the walking behavior of bipedal robot with adjustable step length. Objectives of this paper are threefold. (1) Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) is modified to improve its performance.(2) Self-adaptive Differential Evolutionary Algorithm (SaDE) is applied to search feasible walking gait. (3) An efficient method is proposed for adjusting step length based on the modified central pattern generator (CPG) model. The GAOFSF is modified to ensure that trajectories generated are continuous in angular position, velocity, and acceleration. After formulation of the modified CPG model, SaDE is chosen to optimize walking gait (CPG model) due to its superior performance. Through simulation results, dynamic balance of the robot with modified CPG model is better than the original one. In this paper, four adjustable factors ( hs,support , hs,swing , ks,support , and ks,swing ) are added to the joint trajectories. Through adjusting these four factors, joint trajectories are changed and hence the step length achieved by the robot. Finally, the relationship between (1) the desired step length and (2) an appropriate set of hs,support , hs,swing , ks,support , and ks,swing searched by SaDE is learnt by Fuzzy Inference System (FIS). Desired joint angles can be found without the aid of inverse kinematic model.

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Section: Introductionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

Fung

Jing

2014

ISRN Robotics

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“…Moreover, for most of the scenarios considered, the DE operator did not improve the performance of the original MOEA/D. Since related work has shown that DE variation can often improve the performance of other algorithms [22], we hypothesize that the interaction between the decomposition approach and DE is responsible for this.…”

Section: Discussionmentioning

confidence: 83%

Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms

Bezerra

López-Ibáñez

Stützle

2015

Lecture Notes in Computer Science

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Abstract.A main focus of current research on evolutionary multiobjective optimization (EMO) is the study of the effectiveness of EMO algorithms for problems with many objectives. Among the several techniques that have led to the development of more effective algorithms, decomposition and component-wise design have presented particularly good results. But how do they compare? In this work, we conduct a systematic analysis that compares algorithms produced using the MOEA/D decomposition-based framework and the AutoMOEA component-wise design framework. In particular, we identify a version of MOEA/D that outperforms the best known MOEA/D algorithm for several scenarios and confirms the effectiveness of decomposition on problems with three objectives. However, when we consider problems with five objectives, we show that MOEA/D is unable to outperform SMS-EMOA, being often outperformed by it. Conversely, automatically designed AutoMOEAs display competitive performance on three-objective problems, and the best and most robust performance among all algorithms considered for problems with five objectives.

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“…Therefore, a more accurate methodology for coordinate optimization is highly desirable, as investigated herein. The evaluation of εDE as an optimization method for this critical task was inspired by previous results obtained for other types of optimization problems where this approach displayed better performance than alternative evolutionary methods, such as genetic algorithms or particle swarm optimization [27][28][29] . Moreover, ε-based lexicological comparison of individual feature vectors makes this algorithm straightforward to apply to problems where several constraints must be balanced, as is the case in inverse QSAR.…”

Section: Resultsmentioning

confidence: 99%

Exploring differential evolution for inverse QSAR analysis

2017

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Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

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Differential Evolution versus Genetic Algorithms in Multiobjective Optimization

Cited by 103 publications

References 16 publications

An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

Comparing Decomposition-Based and Automatically Component-Wise Designed Multi-Objective Evolutionary Algorithms

Exploring differential evolution for inverse QSAR analysis

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