A Fuzzy Goal Programming Procedure for Solving Multiobjective Load Flow Problems via Genetic Algorithm
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This paper describes how the genetic algorithms (GAs) can be efficiently used to fuzzy goal programming (FGP) formulation of optimal power flow problems having multiple objectives. In the proposed approach, the different constraints, various relationships of optimal power flow calculations are fuzzily described.In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first for measuring the degree of achievement of the aspiration levels of the goals specified in the decision making context. Then, the achievement function for minimizing the regret for under‐deviations from the highest membership value (unity) of the defined membership goals to the extent possible on the basis of priorities is constructed for optimal power flow problems.In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. In the GA based solution search process, the conventional Roulette wheel selection scheme, arithmetic crossover and random mutation are taken into consideration to reach a satisfactory decision.The developed method has been tested on IEEE 6‐generator 30‐bus System. Numerical results show that this method is promising for handling uncertain constraints in practical power systems.Keywords:
Roulette
Fitness proportionate selection
Roulette wheel method is the base of Genetic Algorithm(GA),and it is firmly related to the operations of reproduction and crossover.This paper researches into the optimization for roulette wheel method from the view of pure Genetic Algorithm,and analyzes the large numbers of actual computing results by the tools of database and statistic.The analysis indicates that it can obviously improve the successful probability of obtaining global optimization result as well as the computing efficiency while using the improved roulette wheel method in Genetic Algorithm.
Roulette
Fitness proportionate selection
Statistic
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This study presents genetic algorithm (GA) to solve routing problem modelled as the
travelling salesman problem (TSP). Genetic algorithm conceptually follows steps
inspired by the biological process of evolution. GA is following the ideas of survival
of the fittest which meant better and better solution evolves from previous generations
until a near optimal solution is obtained. In TSP, There are cities and distance given
between the cities. The salesman needs to visit all the cities, but does not to travel so
much. This study will use PCB component placement which is modelled as TSP. The
objective is to find the sequence of the routing in order to minimize travelling distance.
The GA with Roulette wheel selection, linear order crossover and inversion mutation
is used in the study. The computational experiment was done using several randomly
generated data with different GA parameter setting. The optimal distance obtains for
40 component placements is 8.9861 mm within 6.751 seconds The results from the
experiments show that GA used in this study is effective to solve PCB component
placement which is modelled as TSP.
Roulette
Component (thermodynamics)
Fitness proportionate selection
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Abstract The reasonable and optimized navigation for an automatic pilot ship is one of the key technologies in ship bridge system. In this paper, a genetic algorithm based path planning method is introduced for this kind of problem. In order to build efficient chromosome to improve the searching efficiency, before the planning procedure of the GA, a traditional but efficient fast searching method - RRT is utilized to give a clue to build the chromosomes. In this paper, at first, a searching area is constructed based on the result of RRT and secondly, a GA based planning algorithm is proposed for the ship to find a path from the start to a target. The fitness function and genetic operator are selected by roulette to ensure the robustness of genetic algorithm. The diversity of population is enhanced by two point crossover operators. Finally, the simulation results show the effectiveness of the proposed algorithm.
Roulette
Robustness
Fitness proportionate selection
Evaluation function
Genetic operator
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Genetic algorithm (GA) has several genetic operators that can be changed to improve the performance of particular implementations.These operators include selection, crossover and mutation.Selection is one of the important operations in the GA process.There are several ways for selection like Roulette-Wheel, Rank, and Tournament etc.This paper presents a new selection operator based on alpha cut as in Fuzzy Logic.This is compared with other selection in solving travelling salesman problem (TSP) using different parent selection strategy.Several TSP instances were tested and the results show that proposed selection outperformed proportional roulette wheel, achieving best solution quality with low computing times.
Alpha (finance)
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The space allocation problem particularly in academic institutions is well known as a complex, difficult and time consuming task. This paper investigates the optimum genetic operators’ selection method in genetic algorithm in handling combinatorial optimisation problems. The discussed space allocation problem focuses on the distribution of timetable among available lecture halls and rooms in the university. We investigate the optimum selection method to select parents during the reproduction and crossover genetic operations. The investigated selection methods are random, deterministic and roulette wheel selection.
Fitness proportionate selection
Roulette
Truncation selection
Genetic operator
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Abstract The purpose of this study is to investigate the use of genetic algorithms (GAs) for airvehicle control system design and optimization. The design of the controller gains for the longitudinal channel of an autonomous helicopter model is formulated and solved as a complex, constrained, nonlinear, multiple objective problem. GAs are implemented using both binary and floating point representations with associated mutation and crossover operators. Selection is based on a roulette wheel technique. Improvements of the algorithm performance are obtained by the use of selective weights in the evaluation function and elitist selection strategy.
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Fitness proportionate selection
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To address the shortcomings of the traditional genetic algorithm in cruise missile route planning, which is prone to "premature maturation" and premature convergence to a local optimal solution, an improved genetic algorithm is proposed that introduces an adaptive operator and a variation ratio strategy. The algorithm processes the individual fitness values in the population by ranking ratio technique, and then adopts a selection strategy combining elite selection and roulette algorithm to add the feasible routes with the best fitness values directly to the children at each evolution, and then roulette selects the remaining feasible routes, which improves the global optimal search performance of the algorithm in the trajectory planning. Meanwhile, an adaptive crossover operator is used to dynamically select the crossover probability based on the individual fitness values of the parents. Finally, the two algorithms are applied to the established map model for route planning separately, and the simulation results show that the path solved by the improved genetic algorithm reduces three planning waypoints and 10.74 % of the range compared with the traditional genetic algorithm, and the global search performance of the route planning process applying the improved genetic algorithm is significantly better than that of the traditional genetic algorithm.
Cruise missile
Route Planning
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In order to effectively overcome the disadvantages of traditional Genetic Algorithm which converge slowly and easily run into local extremism,an improved crossover operator of genetic algorithms was proposed.This operator used the autoadaptive crossover probability and entrusted individual having big irrelevance index with a big elected probability to carry on the crossing operation;The two generations competitive selective operator was designed to improve the traditional genetic algorithm based on roulette.In a simulative example of multi-peaks function,the proposed method can reduce useless crossover effectively and thus the convergence speed and the search capability are greatly improved when compared with the elitist reserved genetic algorithm that keeps best strategy.As a result,the average convergence generations and the probability of getting optimal result are superior to the elitist reserved genetic algorithm.
Roulette
Operator (biology)
Fitness proportionate selection
Genetic operator
Local optimum
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This paper designed a mathematical model of vehicle synthetising and proposed a solution to solve the vehicle carpool problem by using genetic algorithm. The solution encoded the nodes of station and start, end point. Arranged randomly to generate multiple chromosome, initial population and calculate the fitness value obtained from the objective function by the matching node of each vehicle. Generated a new individual by mutation and crossover through Roulette Wheel Selection method.Repeat the above operation to reach the maximun number of iteration and got the optimal solution path. The experimental result shows that the algorithm is valid.
Fitness proportionate selection
Roulette
Carpool
Evaluation function
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In this paper, we construct a genetic algorithm (GA) for location problems of urban facilities. In the encoding of the GA, loci and alleles are defined as sites for placements and types of the facilities, respectively. An individual is a planar array. The genetic operators are selection, crossover and mutation. In the selection, roulette selection and elitist preserving selection are used. In the crossover, 2 selected individuals are each divided into 4 by 2 straight lines which are selected at random. One of the 4 divided parts is selected at random. The selected part is changed between the 2 individuals. In the mutation, a facility or a residence is randomly placed in the randomly selected locus. For fitness, the GA uses the results of the evaluating system which we have proposed. We execute simulation for placement of urban facilities and consider the results of the simulation.
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