scholarly journals A Competitive Comparison of Different Types of Evolutionary Algorithms

2009 ◽  
Author(s):  
O. Hrstka ◽  
A. Kucerová ◽  
M. Leps ◽  
J. Zeman
Keyword(s):  
Evolutionary Algorithms ◽  
Different Types

Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems

2002 ◽  
Vol 34 (4) ◽  
pp. 639-656 ◽  
Author(s):  
Ningchuan Xiao ◽  
David A Bennett ◽  
Marc P Armstrong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Decision Problems ◽  
Graph Representation ◽  
Search Problems ◽  
Site Search ◽  
Conflicting Objectives ◽  
Different Types ◽  
Critical Issues ◽  
A Site

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.

Design of energy efficient RAL system using evolutionary algorithms

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Mukund J Nilakantan ◽  
S G Ponnambalam ◽  
Jawahar N
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Total Energy ◽  
Design Methodology ◽  
Assembly Line ◽  
Robotic Assembly ◽  
Total Energy Consumption ◽  
Swarm Optimization ◽  
Different Types

Purpose Manufacturing industries these days gives importance to reduce the energy consumption due to the increase in energy prices and to create an environmental friendly industry. Robotic assembly lines (RALs) are used in an industry for assembling different types of products in an assembly line due to the flexibility it offers to the production system. Since different types of robots are available with different specialization and capabilities, there is a requirement of efficiently balancing the assembly line by allocating equal amount of tasks to the workstations and allocate the best fit robot to perform the allocated tasks. The goal of this paper is to maximize the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line. Design/methodology/approach Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem. Performance of these proposed algorithm are tested on a set of randomly generated problems which are generated using the benchmark problems available in the open literature and the results are reported. Findings The proposed algorithm are found to be useful to reduce the total energy consumption on an assembly line which maximizes the line efficiency. It is found that DE algorithm could improve the line efficiency than PSO algorithm. Computational time taken by the two algorithm are also reported. Originality/value Till date, no research has been reported on optimizing the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line systems. Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem.

scholarly journals Multiobjective shape optimization of selected coupled problems by means of evolutionary algorithms

2012 ◽  
Vol 60 (2) ◽  
pp. 215-222 ◽  
Author(s):  
A. Długosz ◽  
T. Burczyński
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Evolutionary Algorithms ◽  
Field Analysis ◽  
Coupled Problems ◽  
Parametric Curves ◽  
Numerical Examples ◽  
Thermal Fields ◽  
Coupled Field ◽  
Different Types

Abstract. In present paper an improved multi-objective evolutionary algorithm is used for Pareto optimization of selected coupled problems. Coupling of mechanical, electrical and thermal fields is considered. Boundary-value problems of the thermo-elasticity, piezoelectricity and electro-thermo-elasticity are solved by means of finite element method (FEM). Ansys Multiphysics and MSC.Mentat/Marc software are used to solve considered coupled problems. Suitable interfaces between optimization tool and the FEM software are created. Different types of functionals are formulated on the basis of results obtained from the coupled field analysis. Functionals depending on the area or volume of the structure are also proposed. Parametric curves NURBS are used to model some optimized structures. Numerical examples for exemplary three-objective optimization are presented in the paper.

scholarly journals A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Realizations and Predictability

2007 ◽  
Vol 15 (4) ◽  
pp. 435-443 ◽  
Author(s):  
Jun He ◽  
Colin Reeves ◽  
Carsten Witt ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithms ◽  
Polynomial Time ◽  
Fitness Landscape ◽  
Fitness Function ◽  
Black Box ◽  
Landscape Analysis ◽  
Problem Difficulty ◽  
Fitness Landscape Analysis ◽  
Theoretical Assumptions ◽  
Different Types

Various methods have been defined to measure the hardness of a fitness function for evolutionary algorithms and other black-box heuristics. Examples include fitness landscape analysis, epistasis, fitness-distance correlations etc., all of which are relatively easy to describe. However, they do not always correctly specify the hardness of the function. Some measures are easy to implement, others are more intuitive and hard to formalize. This paper rigorously defines difficulty measures in black-box optimization and proposes a classification. Different types of realizations of such measures are studied, namely exact and approximate ones. For both types of realizations, it is proven that predictive versions that run in polynomial time in general do not exist unless certain complexity-theoretical assumptions are wrong.

scholarly journals AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

2020 ◽  
Vol 8 (5) ◽  
pp. 300 ◽  
Author(s):  
Rafael Guardeño ◽  
Manuel J. López ◽  
Jesús Sánchez ◽  
Agustín Consegliere
Keyword(s):  
Mathematical Model ◽  
Evolutionary Algorithms ◽  
Numerical Simulations ◽  
Obstacle Avoidance ◽  
Laser Imaging ◽  
Unmanned Surface Vehicles ◽  
Sensor Model ◽  
Low Load ◽  
Different Types ◽  
Imaging Detection

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

Generation of Diversity in a Reaction-Diffusion-Based Controller

2014 ◽  
Vol 20 (3) ◽  
pp. 319-342 ◽  
Author(s):  
Payam Zahadat ◽  
Thomas Schmickl
Keyword(s):  
Evolutionary Algorithms ◽  
Solution Space ◽  
Control Unit ◽  
Reaction Diffusion ◽  
Point Of View ◽  
Early Step ◽  
Different Types ◽  
And Behaviors ◽  
Hormone System

A controller of biological or artificial organism (e.g., in bio-inspired cellular robots) consists of a number of processes that drive its dynamics. For a system of processes to perform as a successful controller, different properties can be mentioned. One of the desirable properties of such a system is the capability of generating sufficiently diverse patterns of outputs and behaviors. A system with such a capability is potentially adaptable to perform complicated tasks with proper parameterizations and may successfully reach the solution space of behaviors from the point of view of search and evolutionary algorithms. This article aims to take an early step toward exploring this capability at the levels of individuals and populations by introducing measures of diversity generation and by evaluating the influence of different types of processes on diversity generation. A reaction-diffusion-based controller called the artificial homeostatic hormone system (AHHS) is studied as a system consisting of different processes with various domains of functioning (e.g., internal or external to the control unit). Various combinations of these processes are investigated in terms of diversity generation at levels of both individuals and populations, and the effects of the processes are discussed representing different influences for the processes. A case study of evolving a multimodular AHHS controller with all the various process combinations is also investigated, representing the relevance of the diversity generation measures and practical scenarios.

Evolution Algorithms and Biomass Properties Prediction: A Review

2019 ◽  
Author(s):  
Obafemi Olatunji ◽  
Stephen Akinlabi ◽  
Nkosinathi Madushele ◽  
Paul Adedeji ◽  
Samuel Fatoba
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Biomass Energy ◽  
Cumulative Impact ◽  
Future Directions ◽  
Prediction Technique ◽  
Real World Applications ◽  
Different Types ◽  
Evolution Algorithms ◽  
Hybrid Forms

Abstract The complexity of real-world applications of biomass energy has increased substantially due to so many competing factors. There is an ongoing discussion on biomass as a renewable energy source and its cumulative impact on the environment vis-a-vis water competition, environmental pollution and so on. This discussion is coming at a time when evolutionary algorithms and its hybrid forms are gaining traction in several applications. In the last decade, evolution algorithms and its hybrid forms have evolved as a significant optimization and prediction technique due to its flexible characteristics and robust behaviour. It is very efficient means of solving complex global optimization problems. This article presents the state-of-the-art review of different types of evolutionary algorithms, which have been applied in the prediction of major properties of biomass such as elemental compositions and heating values. The governing principles, applications, merits, and challenges associated with this technique are elaborated. The future directions of the research on biomass properties prediction are discussed.

scholarly journals A competitive comparison of different types of evolutionary algorithms

2003 ◽  
Vol 81 (18-19) ◽  
pp. 1979-1990 ◽  
Author(s):  
O. Hrstka ◽  
A. Kučerová ◽  
M. Lepš ◽  
J. Zeman
Keyword(s):  
Evolutionary Algorithms ◽  
Different Types
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