scholarly journals Genetic Algorithms as Computational Methods for Finite-Dimensional Optimization

2021 ◽  
pp. 5-14
Author(s):  
Nataliya Gulayeva ◽  
Volodymyr Shylo ◽  
Mykola Glybovets
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Methods ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Direct Search ◽  
Mathematical Programming Problem ◽  
Combinatorial Optimization Problems

Introduction. As early as 1744, the great Leonhard Euler noted that nothing at all took place in the universe in which some rule of maximum or minimum did not appear [12]. Great many today’s scientific and engineering problems faced by humankind are of optimization nature. There exist many different methods developed to solve optimization problems, the number of these methods is estimated to be in the hundreds and continues to grow. A number of approaches to classify optimization methods based on various criteria (e.g. the type of optimization strategy or the type of solution obtained) are proposed, narrower classifications of methods solving specific types of optimization problems (e.g. combinatorial optimization problems or nonlinear programming problems) are also in use. Total number of known optimization method classes amounts to several hundreds. At the same time, methods falling into classes far from each other may often have many common properties and can be reduced to each other by rethinking certain characteristics. In view of the above, the pressing task of the modern science is to develop a general approach to classify optimization methods based on the disclosure of the involved search strategy basic principles, and to systematize existing optimization methods. The purpose is to show that genetic algorithms, usually classified as metaheuristic, population-based, simulation, etc., are inherently the stochastic numerical methods of direct search. Results. Alternative statements of optimization problem are given. An overview of existing classifications of optimization problems and basic methods to solve them is provided. The heart of optimization method classification into symbolic (analytical) and numerical ones is described. It is shown that a genetic algorithm scheme can be represented as a scheme of numerical method of direct search. A method to reduce a given optimization problem to a problem solvable by a genetic algorithm is described, and the class of problems that can be solved by genetic algorithms is outlined. Conclusions. Taking into account the existence of a great number of methods solving optimization problems and approaches to classify them it is necessary to work out a unified approach for optimization method classification and systematization. Reducing the class of genetic algorithms to numerical methods of direct search is the first step in this direction. Keywords: mathematical programming problem, unconstrained optimization problem, constrained optimization problem, multimodal optimization problem, numerical methods, genetic algorithms, metaheuristic algorithms.

scholarly journals Application of Genetic Algorithm in Common Optimization Problems

2019 ◽  
Vol 8 (1) ◽  
pp. 17-21
Author(s):  
Nika Topuria ◽  
Omar Kikvidze
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Local Optima ◽  
Deterministic Algorithms ◽  
Complex Optimization ◽  
Short Time ◽  
Fitness Parameter

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

Research on the Application of College Automated Course Scheduling System Based on Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1782-1785
Author(s):  
Xiu Ying Li ◽  
Dong Ju Du
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Teaching Quality ◽  
Combinatorial Optimization Problem ◽  
Scheduling System ◽  
Course Scheduling ◽  
Strong Ability

A reasonable curriculum contributes to the improvement of the training and teaching quality of college students. Using computer which is speed and strong ability to arrange curriculum automatically is imperative. Automatically curriculum arrangement is a constrained, multi-objective and intricate combinatorial optimization problem. Based on genetic algorithm of population search, it is suitable to process complex and nonlinear optimization problems which it difficult to solve for traditional search methods. In this paper solves complex automated course scheduling using genetic algorithms.

Layout Optimization of a Multiple Pinned Joint Under Bending in a Limited Contact Area

2013 ◽  
Author(s):  
Amir Mohsen Hejazi ◽  
Mohammad Pourgol Mohammad
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Stress Concentration ◽  
Stress Analysis ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Limited Area ◽  
Design Factor ◽  
Finite Element Software

Layout determination of connectors in different mechanical configurations improves the design characteristics. The issue has recently become more practical in sensitive industries, especially in montage processes. Since connections are under different loads like bending, the layout of connection should be considered as an effective design factor in different loading conditions which is itself a step forward in achieving the optimized connection and also increases the connection life. This paper analyses the layout effects in a multiple pinned joint under bending in a limited area. The goal is to minimize the average stress and having a uniform stress distribution in the connections in order to prevent the failure inducing effect of stress concentration. The common method for solving these optimization problems is to couple two finite element numerical stress analysis software with an optimization tool or independent software which is a highly time consuming method due to enormous volume of the calculations in each iteration. In this paper the optimization problem is mathematically modeled and solved using Genetic Algorithm (GA). Genetic algorithm is found applicable here due to nonlinear behavior and complexity of the objective function in the optimization problem where analytical optimization methods are not useful. The validation results of stress analysis are obtained using finite element software. The optimized connections have longer lifetime and can carry higher loads because of degraded effects of stress concentration and minimized stresses.

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

scholarly journals Optimization Methods and Algorithms for Calculating the Construction of Non-Rigid Pavement for Technological Quarries Roads

2019 ◽  
Vol 134 ◽  
pp. 01007
Author(s):  
Anna Tailakova ◽  
Alexander Pimonov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Programming ◽  
Computational Experiment ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Rigid Pavement ◽  
The Stability ◽  
Optimal Construction ◽  
Selection Of

Previously developed by the authors of the optimization model for calculating the construction of non-rigid pavement of public roads is proposed to be used for the calculation of the construction of pavement technological career roads. The article describes the optimization methods and algorithms for calculating the construction of pavement. Possibilities of using methods of coordinate-wise descent, multi-start, dynamic programming for the selection of the optimal construction of pavement are presented. Application of genetic algorithms for the decision of an optimization problem of calculation of a construction of pavements on the basis of comparison of their efficiency with efficiency of search methods is proved. Described results of computational experiment of selection of genetic algorithm operators to reduce the volume of calculations and ensure the stability of the results.

scholarly journals An Optimal Design Methodology for the Trajectory of Hydraulic Excavators Based on Genetic Algorithm

2021 ◽  
Vol 33 (6) ◽  
pp. 1248-1254
Author(s):  
Takamichi Yuasa ◽  
◽  
Masato Ishikawa ◽  
Satoshi Ogawa
Keyword(s):  
Genetic Algorithm ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Reaction Force ◽  
Ground Level ◽  
Optimization Method ◽  
Work Efficiency ◽  
Cost Index ◽  
Hydraulic Excavators

Hydraulic excavators are one type of construction equipment used in various construction sites worldwide, and their usage and scale are diverse. Generally, the work efficiency of a hydraulic excavator largely depends on human operation skills. If we can comprehend the experienced operation skills and utilize them for manual control assist, semi-automatic or automatic remote control, it would improve its work efficiency and suppress personnel costs, reduce the operator’s workload, and improve his/her safety. In this study, we propose a methodology to design efficient machine trajectories based on mathematical models and numerical optimization, focusing on ground-level excavation as a dominant task. First, we express its excavation trajectory using four parameters and assume the models for the amount of excavated soil and the reaction force based on our previous experiments. Next, we combine these models with a geometrical model for the hydraulic excavating machine. We then assign the amount of soil to a performance index preferably to be maximized and the amount of work to a cost index preferably to be minimized, both in the form of functions of the trajectory parameters, resulting in an optimization problem that trades them off. In particular, we formulate (1) a multi-objective optimization problem maximizing a weighted linear combination of the amount of soil and the amount of work as an objective function, and (2) a single-objective optimization problem maximizing the amount of soil under a given upper bound on the amount of work, so that we can solve these optimization problems using the genetic algorithm (GA). Finally, we conclude this paper by suggesting our notice on design methodology and discussing how we should provide the optimization method as mentioned above to the users who operate hydraulic excavators.

A Survey of Quantum Genetic Algorithm for Combinatorial Optimization Problems

2014 ◽  
Vol 568-570 ◽  
pp. 822-826 ◽  
Author(s):  
Feng Mei Wei ◽  
Jian Pei Zhang ◽  
Bing Li ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
The Other ◽  
Quantum Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Combinational Optimization ◽  
Rotation Strategy

Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.

Sign in / Sign up

Export Citation Format

Share Document