Improving the optimal solution of GoYang network – using genetic algorithm and differential evolution

2019 ◽  
Vol 20 (1) ◽  
pp. 95-102 ◽  
Author(s):  
S. N. Poojitha ◽  
Gagandeep Singh ◽  
V. Jothiprakash
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Benchmark Problem ◽  
Future Studies ◽  
Loop Network ◽  
Optimum Solution

Abstract In the present study, an attempt is made to search for better solutions for the Hanoi network, BakRyan network, and GoYang network (GYN) through evolutionary algorithms (EAs) such as genetic algorithm (GA) and differential evolution (DE), which are initially validated using the two-loop network. A detailed note on the classification of available benchmark-problems is reported. The major aim of the study is to improve the optimal solution of GYN, which can emerge as a standard benchmark-problem for future studies. On applying the developed EA models, an improved optimal cost compared with the literature is obtained for GYN. From the results, it is found that DE outshines GA by its better convergence capability and robustness in attaining an optimum solution.

Hookes-Jeeves-Based Variant of Memetic Algorithm

2016 ◽  
pp. 450-475
Author(s):  
Dipti Singh ◽  
Kusum Deep
Keyword(s):  
Genetic Algorithms ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Memetic Algorithms ◽  
Global Optimum ◽  
Benchmark Problems ◽  
New Variant ◽  
Optimum Solution ◽  
Global Optimum Solution

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

Weighted Similarity Classifier Using Differential Evolution and Genetic Algorithm in Weight Optimization

2004 ◽  
Vol 8 (6) ◽  
pp. 591-598 ◽  
Author(s):  
Pasi Luukka ◽  
◽  
Jouni Sampo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Similarity Measures ◽  
High Dimensional ◽  
Weight Optimization ◽  
Large Scale Data ◽  
Data Weighting

We have compared the differential evolution and genetic algorithms in a study of weight optimization for different similarity measures in a task of classification. In a study of high dimensional data weighting similarity measures become of great importance and efforts to study suitable optimizers is needed. In this article we have studied proper weighting of similarity measures in the classification of high dimensional and large scale data. We will show that in most cases the differential evolution algorithm should be used in finding the weights instead of the genetic algorithm.

scholarly journals ON SEARCH CAPABILITIES OF THE DIFFERENTIAL EVOLUTION ALGORITHM

2001 ◽  
Vol 1 ◽  
pp. 288
Author(s):  
Anatoly Sukov
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Search Technique ◽  
Technical Character ◽  
Evolution Algorithm

This paper examines the algorithm of differential evolution that has appeared rather recently. This algorithm ascribed by its developers to a class of evolutionary algorithms is a comparatively non-complicated technique o f solution search as applied to multiparameter optimisation tasks. Nevertheless, there are two essential factors preventing from wide application of the considered solution search technique. One of them lies in the principle of coding vectors (variables) that constitute a population the algorithm works with. The second problem is of pure technical character: in the process of search, stagnation occurs, or impossibility to find new solutions, when there is no optimal solution in the population and the vectors available are not heterogeneous. Besides studying search possibilities (limitations) of the differential evolution, some ways to cope with the problem of stagnation so-as to raise the performance of the algorithm are also suggested.

scholarly journals Optimal source localization problem based on TOA measurements

2017 ◽  
Vol 14 (1) ◽  
pp. 161-176
Author(s):  
Maja Rosic ◽  
Mirjana Simic ◽  
Predrag Pejovic ◽  
Milan Bjelica
Keyword(s):  
Genetic Algorithm ◽  
Lower Bound ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Nonlinear Least Squares ◽  
Global Optimum ◽  
Time Of Arrival ◽  
Newton Raphson ◽  
Optimum Solution

Determining an optimal emitting source location based on the time of arrival (TOA) measurements is one of the important problems in Wireless Sensor Networks (WSNs). The nonlinear least-squares (NLS) estimation technique is employed to obtain the location of an emitting source. This optimization problem has been formulated by the minimization of the sum of squared residuals between estimated and measured data as the objective function. This paper presents a hybridization of Genetic Algorithm (GA) for the determination of the global optimum solution with the local search Newton-Raphson (NR) method. The corresponding Cramer-Rao lower bound (CRLB) on the localization errors is derived, which gives a lower bound on the variance of any unbiased estimator. Simulation results under different signal-to-noise-ratio (SNR) conditions show that the proposed hybrid Genetic Algorithm-Newton-Raphson (GA-NR) improves the accuracy and efficiency of the optimal solution compared to the regular GA.

scholarly journals Hybrid method for achieving Pareto front on economic emission dispatch

2020 ◽  
Vol 10 (4) ◽  
pp. 3358
Author(s):  
Kummari Rajesh ◽  
N. Visali
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Swarm Optimization ◽  
Metaheuristic Methods

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.

Application and Evaluation of Bee-Based Algorithms in Scheduling

2018 ◽  
pp. 20-42
Author(s):  
Ayse Aycim Selam ◽  
Ercan Oztemel
Keyword(s):  
Project Scheduling ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Manufacturing Processes ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Multi Mode ◽  
Vital Element

Scheduling is a vital element of manufacturing processes and requires optimal solutions under undetermined conditions. Highly dynamic and, complex scheduling problems can be classified as np-hard problems. Finding the optimal solution for multi-variable scheduling problems with polynomial computation times is extremely hard. Scheduling problems of this nature can be solved up to some degree using traditional methodologies. However, intelligent optimization tools, like BBAs, are inspired by the food foraging behavior of honey bees and capable of locating good solutions efficiently. The experiments on some benchmark problems show that BBA outperforms other methods which are used to solve scheduling problems in terms of the speed of optimization and accuracy of the results. This chapter first highlights the use of BBA and its variants for scheduling and provides a classification of scheduling problems with BBA applications. Following this, a step by step example is provided for multi-mode project scheduling problem in order to show how a BBA algorithm can be implemented.

scholarly journals Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Guo Zhao ◽  
Xueliang Huang ◽  
Hao Qiang
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Control Strategy ◽  
Optimization Model ◽  
Pareto Front ◽  
Optimal Solution ◽  
Coordinated Control ◽  
Nsga Ii ◽  
Nondominated Solutions ◽  
Pv Generation

Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell) genetic algorithm, an improved differential evolution cellular (IDECell) genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

scholarly journals Penerapan Metode Hybrid Genetic Algorithm (GA) dan Pattern Search (PS) untuk Penjadwalan Mata Kuliah Universitas

2020 ◽  
Vol 9 (3) ◽  
pp. 201-212
Author(s):  
Fani Puspitasari ◽  
Parwadi Moengin
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pattern Search ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Course Scheduling ◽  
Fitness Value ◽  
Optimum Solution ◽  
University Course

The problem of university course scheduling is a complicated job to do because of the many constraints that must be considered, such as the number of courses, the number of rooms available, the number of students, lecturer preferences, and time slots. The more courses that will be scheduled, the scheduling problem becomes more complex to solve. Therefore, it is necessary to set an automatic course schedule based on optimization method. The aim of this research is to gain an optimal solution in the form of schedule in order to decrease the number of clashed courses, optimize room utilization and consider the preferences of lecturer-course. In this research, a hybridization method of Genetic Algorithm (GA) and Pattern Search (PS) is investigated for solving university course scheduling problems. The main algorithm is GA to find the global optimum solution, while the PS algorithm is used to find the local optimum solution that is difficult to obtain by the GA method. The simulation results with 93 courses show that the Hybrid GA-PS method works better than does the GA method without hybrid, as evidenced by the better fitness value of the hybrid GA-PS method which is -3528.62 and 99.24% of the solutions achieved. While the GA method without hybrid is only able to reach a solution of around 65% and has an average fitness value of -3100.76.

Choosing the best parameters for method of deformed stars in n-dimensional space

2021 ◽  
pp. 24-28
Author(s):  
Maryna Antonevych ◽  
Anna Didyk ◽  
Nataliia Tmienova ◽  
Vitaliy Snytyuk
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Classical Method ◽  
Dimensional Space ◽  
New Method ◽  
Evolutionary Strategy ◽  
Point Groups ◽  
Best Parameters

This paper is devoted to the problem of optimization of a function in -dimensional space, which, in general case, is polyextreme and undifferentiated. The new method of deformed stars in n-dimensional space was proposed. It is built on the ideas and principles of the evolutionary paradigm. Method of deformed stars is based on the assumption of using potential solutions groups. There by it allows to increase the rate of the accuracy and the convergence of the achieved result. Populations of potential solutions are used to optimize the multivariable function. In contrast to the classical method of deformed stars, we obtained a method that solves problems in -dimensional space, where the population of solutions consists of 3-, 4-, and 5-point groups. The advantages of the developed method over genetic algorithm, differential evolution and evolutionary strategy as the most typical evolutionary algorithms are shown. Also, experiments were performed to investigate the best configuration of method of deformed stars parameters.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

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