Editorial — Recent Progress in Intelligent and Evolutionary Algorithms and their Applications

2015 ◽  
Vol 11 (02) ◽  
pp. 115-120
Author(s):  
Aki-Hiro Sato ◽  
Hiroshi Kawakami ◽  
Toshihiro Hiraoka
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Recent Progress ◽  
Optimization Problems ◽  
Asia Pacific ◽  
Topical Issue ◽  
Special Issue ◽  
Mathematical Methods ◽  
Evolutionary Systems ◽  
Real World Applications

This is a topical issue on the 16th Asia–Pacific Symposium on Intelligent and Evolutionary Systems (IES) which was held in Kyoto from December 12–14, 2012. This special issue contains six articles related to evolutionary algorithms that are designed to solve optimization problems, network concepts, mathematical methods and their real world applications.

scholarly journals A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

2021 ◽  
Author(s):  
Fei Ming
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Density Estimation ◽  
Real World ◽  
Selection Pressure ◽  
Optimization Problems ◽  
Variable Number ◽  
Objective Functions ◽  
Considerable Research ◽  
Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

scholarly journals A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

2021 ◽  
Author(s):  
Fei Ming
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Density Estimation ◽  
Real World ◽  
Selection Pressure ◽  
Optimization Problems ◽  
Variable Number ◽  
Objective Functions ◽  
Considerable Research ◽  
Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

scholarly journals Editorial – Preface to the Special Issue on Numerical Techniques Meet with OR

2021 ◽  
Vol 46 (1) ◽  
pp. 3-10
Author(s):  
Burcu Gürbüz ◽  
Gerhard-Wilhelm Weber
Keyword(s):  
Real World ◽  
Numerical Algorithms ◽  
Special Issue ◽  
Numerical Techniques ◽  
Research Topics ◽  
Geometrical Structures ◽  
Financial Applications ◽  
Real World Applications ◽  
Research Problems ◽  
Foundations Of Computing

AbstractThis special issue of the Foundations of Computing and Decision Sciences, titled ”Numerical Techniques Meet with OR”, is devoted to the numerical techniques and their applications in real-world phenomena. The special issue and its editorial present numerical algorithms as they meet with different research topics such as, e.g., from operational research, supply chain management, geometrical structures and Covid-19 effects on financial applications. Besides, the special issue covers instructional information about numerical techniques which are useful for OR research problems and real-world applications on such issues.

Understanding the Course and State of Evolutionary Optimizations Using Visualization: Ten Years of Industry Experience with Evolutionary Algorithms

2006 ◽  
Vol 12 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Hartmut Pohlheim
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
The Other ◽  
Simple Manner ◽  
Extra Information ◽  
Real World Applications ◽  
The Difference ◽  
Visualization Techniques ◽  
Industry Experience ◽  
Insight Into

Evolutionary algorithms (EAs) are widely employed to solve a broad range of optimization problems. Even though they work in an algorithmically simple manner, it is not always easy to understand what is going on during a particular optimization run. It is especially desirable to gain further insight into the state and course of the algorithm if the optimization does not yield the expected results or if we are not sure whether the result achieved is really the best result possible. During an optimization run an EA produces a vast amount of data. The extraction of useful information is a nontrivial task. In this article, we review visualization methods used to extract this useful information. We also demonstrate the application of visualization techniques and explain how they help us to understand the course and state of the EA. This extra information gained by the use of visualization techniques is often the difference between a good result and a very good result. In complex real-world applications, merely achieving a good result often means that the approach has failed. On the other hand, a success means large gains in productivity or safety or a decrease in costs.

scholarly journals A repository of real-world datasets for data-driven evolutionary multiobjective optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Cheng He ◽  
Ye Tian ◽  
Handing Wang ◽  
Yaochu Jin
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Data Driven ◽  
Benchmark Problems ◽  
Computational Costs ◽  
Evolutionary Multiobjective Optimization ◽  
Multiobjective Optimization Problems ◽  
Real World Applications ◽  
Real World Datasets

Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

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