scholarly journals Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 45-63
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Individual Characteristics ◽  
Nsga Ii ◽  
Human Agent ◽  
Performance Metric ◽  
Novel Approach ◽  
Distributed Processes ◽  
Real Coded Genetic Algorithm ◽  
Decision Making System

AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., -MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.

scholarly journals HYBRID NSGA-II OPTIMIZATION FOR IMPROVING THE THREE-TERM BP NETWORK FOR MULTICLASS CLASSIFICATION PROBLEMS

2015 ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
Siti Mariyam Shamsuddin ◽  
Sultan Noman Qasem
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Classification Performance ◽  
Classification Problems ◽  
Nsga Ii ◽  
Bp Network ◽  
Multiobjective Evolutionary Algorithm

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.  

scholarly journals Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

2021 ◽  
Vol 21 (3) ◽  
pp. 127-144
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Mutation Operators ◽  
Novel Approach ◽  
Real Coded Genetic Algorithm ◽  
Simulation Based ◽  
Power Mutation

Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

scholarly journals An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

2019 ◽  
Vol 4 (3) ◽  
pp. 291
Author(s):  
Farid Jauhari ◽  
Wayan Firdaus Mahmudy ◽  
Achmad Basuki
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Genetic Parameters ◽  
The Other ◽  
Optimal Solutions ◽  
Tuition Fees ◽  
Nsga Ii ◽  
Chromosome Representation ◽  
Time Required

Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

ON A CLASS OF HYBRID SYSTEMS VIA A NOVEL APPROACH FOR REAL-CODED GENETIC ALGORITHM WITH HYBRID SELECTION

2007 ◽  
Vol 06 (02) ◽  
pp. 315-332 ◽  
Author(s):  
M. SENTHIL ARUMUGAM ◽  
M. V. C. RAO
Keyword(s):  
Genetic Algorithm ◽  
Experimental Approach ◽  
Statistical Tests ◽  
Selection Method ◽  
Forward Algorithm ◽  
Novel Approach ◽  
Real Coded Genetic Algorithm ◽  
Hybrid Selection ◽  
Improved Performance ◽  
The Right

A real-coded genetic algorithm (RGA) approach with hybrid selection method to compute the optimal control and cost (fitness) of a single stage hybrid system is investigated. A typical numerical example is included to illustrate the efficacy of the proposed algorithm, which gives the better solution in comparison with forward algorithm. Several statistical tests are also carried out to prove the improved performance of the proposed algorithm. ANOVA test and t-test are also done to verify the betterment of the RGA with hybrid selection. From the experimental approach of the various proportion of the hybrid selection, the right proportions of the hybrid selection is identified and tested.

scholarly journals Planning the Structure of University Teaching Staff Based on Multiobjective Optimization Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoqian Zhang
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Maximum Entropy Principle ◽  
Teaching Quality ◽  
Colleges And Universities ◽  
University Education ◽  
University Teaching ◽  
Teaching Staff ◽  
Nsga Ii

University education is a hot topic of research in this era of outcome-based education in a learning-centric atmosphere, as people struggle for a higher quality of life and technological advancements. The key problems remain in structuring the teaching staff to achieve optimal information transmission and quality. Existing research aims to improve the quality of teaching of the staff, but majority of them fail to achieve their objectives. Multiobjective (MO) optimization has attracted researchers’ interest, particularly, in the context of performance monitoring and improving teaching quality. The goal of this research is to look into techniques for improving academic accomplishment through the planning structure of university teaching staff. I have adopted the Jaynes maximum entropy principle and fuzzy entropy concept to solve the structural optimization problem in the development of teaching staff in colleges and universities. The objective function and constraints in multiobjective optimization are determined, and the multiobjective optimization issue in the development of teaching staff structure is solved using the nondominated sorting genetic algorithm (NSGA-II) multiobjective genetic algorithm. The results show that the optimized structure of the teaching staff can reflect the goal of the construction of the teaching staff in colleges and universities and provide a scientific basis for the construction and planning of the teaching staff.

Progressive-Stepping-Based Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization

2016 ◽  
Vol 7 (3) ◽  
pp. 17-49 ◽  
Author(s):  
Akshay Baviskar ◽  
Shankar Krishnapillai
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Multi Objective Optimization ◽  
Step Size ◽  
Nsga Ii ◽  
Multi Objective ◽  
Performance Metric ◽  
Mechanical Design Problem ◽  
Utopia Point

This paper demonstrates two approaches to achieve faster convergence and a better spread of Pareto solutions in fewer numbers of generations, compared to a few existing algorithms, including NSGA-II and SPEA2 to solve multi-objective optimization problems (MOP's). Two algorithms are proposed based on progressive stepping mechanism, which is obtained by the hybridization of existing Non-dominated Sorting Genetic Algorithm II (NSGA-II) with novel guided search schemes, and modified chromosome selection and replacement mechanisms. Progressive Stepping Non-dominated Sorting based on Local search (PSNS-L) controls the step size, and Progressive Stepping Non-dominated Sorting based on Utopia point (PSNS-U) method controls the number of divisions to generate better chromosomes in each generation to achieve faster convergence. Four multi-objective evolutionary algorithms (EA's) are compared for different benchmark functions and PSNS outperforms them in most cases based on various performance metric values. Finally a mechanical design problem has been solved with PSNS algorithms.

A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting

2018 ◽  
Vol 26 (1) ◽  
pp. 89-116 ◽  
Author(s):  
Patrik Gustavsson ◽  
Anna Syberfeldt
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Large Population ◽  
Divide And Conquer ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Sorting Algorithms ◽  
Population Sizes ◽  
Speed Up

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

scholarly journals A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenqiang Yang ◽  
Zhanlei Peng ◽  
Wei Feng ◽  
Muhammad Ilyas Menhas
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicles ◽  
Economic Dispatch ◽  
Optimal Level ◽  
Discharge Management ◽  
Real Coded Genetic Algorithm ◽  
High Efficient ◽  
Dynamic Economic Dispatch ◽  
Handling Method

Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with PEVs (DED with PEVs) determines the optimal level of online units and PEVs, to minimize the fuel cost and grid fluctuations. Considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear, and non-convex characteristics. High efficient DED method provides a powerful tool in both power system scheduling and PEVs charging coordination. In this study, firstly, PEVs are integrated into the DED problem, which can carry out orderly charge and discharge management to improve the quality of the grid. To tackle this, a novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based on feature intervals (GADMFI), is proposed to enhance the exploitation and exploration of conventional RCGAs. Thirdly, a simple and efficient constraint handling method is proposed for an infeasible solution for DED. Finally, the proposed method is compared with the current literature on six cases with three scenarios, including only thermal units, units with disorderly PEVs, and units with orderly PEVs. The proposed GADMFI shows outstanding advantages on solving the DED with/without PEVs problem, obtaining the effect of cutting peaks and filling valleys on the DED with orderly PEVs problem.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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