On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems

2020 ◽  
Author(s):  
Michal W. Przewozniczek ◽  
Bartosz Frej ◽  
Marcin M. Komarnicki
Keyword(s):  
Evolutionary Algorithms ◽  
Separable Problems ◽  
Linkage Learning

Analysis of Solution Quality of Multiobjective Evolutionary Algorithms for Service Restoration in Distribution Systems

2017 ◽  
Vol 28 (6) ◽  
pp. 796-805
Author(s):  
Danilo Sipoli Sanches ◽  
Marcelo Favoretto Castoldi ◽  
João Bosco Augusto London ◽  
Alexandre Cláudio Botazzo Delbem
Keyword(s):  
Evolutionary Algorithms ◽  
Distribution Systems ◽  
Multiobjective Evolutionary Algorithms ◽  
Solution Quality ◽  
Service Restoration

Modeling the Performance of Evolutionary Algorithms on the Root Identification Problem: A Case Study with PBIL and CHC Algorithms

2011 ◽  
Vol 19 (1) ◽  
pp. 107-135 ◽  
Author(s):  
Enrique Yeguas ◽  
Robert Joan-Arinyo ◽  
María Victoria Luzón
Keyword(s):  
Evolutionary Algorithms ◽  
Identification Problem ◽  
Parametric Modeling ◽  
Constraint Satisfaction Problems ◽  
Performance Model ◽  
General Constraint ◽  
Search Spaces ◽  
Number Of Iterations

The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model would compute an index of the quality of the solution reached by the algorithm as a function of run-time. Conversely, if we fix an index of quality for the solution, the model would give the number of iterations to be expected. In this work, we develop a statistical model to describe the performance of PBIL and CHC evolutionary algorithms applied to solve the root identification problem. This problem is basic in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The performance model is empirically validated over a benchmark with very large search spaces.

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.

Simultaneous Tolerance Synthesis for Manufacturing and Quality using Evolutionary Algorithms

2011 ◽  
Vol 2 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Y. S. Rao ◽  
C. S. P. Rao ◽  
G. Ranga Janardhana ◽  
Pandu R. Vundavilli
Keyword(s):  
Evolutionary Algorithms ◽  
Product Design ◽  
Manufacturing Industry ◽  
Tolerance Allocation ◽  
Manufacturing Cost ◽  
Quality Loss ◽  
Mechanical Assembly ◽  
Swarm Optimization ◽  
Single Objective

Tolerance plays a major role in the manufacturing industry, as it affects product design, manufacturing, and quality of the product. This paper considers product design, manufacturing, and quality simultaneously, and introduces a concurrent approach for tolerance allocation using evolutionary algorithms. A non-linear model that minimizes the combination of manufacturing cost and quality loss simultaneously, in a single objective function has been considered. In the proposed work, evolutionary algorithms (that is, Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO)) have been used to determine the optimal tolerances at the minimum manufacturing and quality loss cost. The application of the proposed methodology has been demonstrated on a simple mechanical assembly.

Customers’ Perception Towards Services of Telecommunications Operators

2017 ◽  
Vol 6 (3) ◽  
pp. 146
Author(s):  
Driss Ait Omar ◽  
Mohamed Baslam ◽  
Mourad Nachaoui ◽  
And Mohamed Fakir
Keyword(s):  
Mathematical Modeling ◽  
Inverse Problem ◽  
Quality Of Service ◽  
Decision Support ◽  
Evolutionary Algorithms ◽  
Optimization Problem ◽  
The Real ◽  
The Difference ◽  
Telecommunications Market

<p>Currently the operators in the telecommunications market present offers of subscription to the consumers,and given that competition is strong in this area, most of these advertising offers are prepared to attract and / or keep customers.</p><p>For this reason, customers face problems in choosing operators that meet their needs in terms of price, quality of service (QoS), etc..., while taking into account the margin between what is advertising and what is real. Therefore, we are led to solve a problem of decision support. Mathematical modeling of this problem led to the solution of an inverse problem. Specifi-cally, the inverse problem is to find the real Quality of Service (QoS) function knowing the theoretical QoS. To solve this problem we have reformulated in an optimization problem of minimizing the difference between the real quality of service (QoS) and theoretical (QoS). This model will help customers who seek to know the degree of sincerity of Their operators, as well as it is an opportunity for operators who want to maintain their resources so that they gain the trust of customers. The resulting optimization problem is solved using evolutionary algorithms. The numerical results showed the reliability and credibility of our inverse model and the performance and effectiveness of our approach.</p>

scholarly journals Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Lu ◽  
Jun Ren ◽  
Zhiguang Wang
Keyword(s):  
Experimental Data ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Search Space ◽  
Research Field ◽  
Benchmark Problems ◽  
Regression Problem ◽  
Mathematical Expressions ◽  
The Right

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, sinceGPhas to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge andGP(PFK-GP) is proposed to reduce the space ofGPsearching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population,PFK-GPfinds the right formulas quickly by exploring the search space of data features. We have comparedPFK-GPwith ParetoGPon regression of eight benchmark problems. The experimental results confirm that thePFK-GPcan reduce the search space and obtain the significant improvement in the quality of SR.

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.

An exploration-enhanced elephant herding optimization

2019 ◽  
Vol 36 (9) ◽  
pp. 3029-3046 ◽  
Author(s):  
Islam A. ElShaarawy ◽  
Essam H. Houssein ◽  
Fatma Helmy Ismail ◽  
Aboul Ella Hassanien
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Search Space ◽  
Exploration And Exploitation ◽  
Content Type ◽  
Discrepancy Measure ◽  
The One ◽  
Star Discrepancy ◽  
Exploration Phase

Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

An Information-Theoretic Entropy Metric for Assessing Multi-Objective Optimization Solution Set Quality

2003 ◽  
Vol 125 (4) ◽  
pp. 655-663 ◽  
Author(s):  
Ali Farhang-Mehr ◽  
Shapour Azarm
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Frontier ◽  
Optimization Techniques ◽  
Solution Set ◽  
Multi Objective Optimization ◽  
Information Theoretic ◽  
Multi Objective ◽  
Speed Reducer ◽  
Quantitative Basis

An entropy-based metric is presented that can be used for assessing the quality of a solution set as obtained from multi-objective optimization techniques. This metric quantifies the “goodness” of a set of solutions in terms of distribution quality over the Pareto frontier. The metric can be used to compare the performance of different multi-objective optimization techniques. In particular, the metric can be used in analysis of multi-objective evolutionary algorithms, wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multi-objective design optimization of a speed-reducer, is provided to demonstrate an application of the proposed entropy metric.

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