Solving truck allocation problem in sugar cane industry by genetic algorithms

Based on Latin Hypercube Sampling method, the crossover operation in GA is redesigned; combined with immune mechanism, chromosome concentration is defined and clonal selection strategy is designed, thus an Immune Genetic Algorithm is given based on Latin Hypercube Sampling for solving the Maximum Clique Problem in this paper. The examples shows the new algorithm in solution quality, convergence speed, and other indicators is better than the classical genetic algorithm and good point set genetic algorithm. On the other hand, the new algorithm is not inferior to such classical algorithms as dynamic local search, and it got better solutions to some examples.KeywordsMaximum Clique Problem(MCP)Genetic Algorithm (GA)Latin Hypercube Sampling(LHS)Artificial Immune Systems(AIS)Immune Genetic Algorithm based on Latin Hypercube Sampling(LIGA)

Genetic algorithm is heuristic searching algorithm which based on nature selection of mechanism and nature genetic. The basic concept that inspires the genetic algorithm is that evolution theory. In the process of searching the best solution on classic genetic algorithm often occurs optimum local. Optimum local is a common problem which is often occurs in genetic algorithm, and one of the reasons is that because of the population diversity. If population diversity is too low is led to optimum local, and if it is too high caused more times to look for the best solution. Mutation operator plays an important role in the process of genetic algorithm to manage that population diversity and an important element of mutation operator is mutation rate. On classic genetic algorithm that mutation rate is set in the beginning while the process of genetic algorithm depends on how many generations are. Therefore is needed to control mutation rate in generation. Controlling mutation operator, especially mutation rate based on Fuzzy Logic Controller (FLC) to manage population diversity, not too high or too low, in order to get optimal result. Evaluation is done 10 times execution by comparing the performance of standard genetic algorithm (GA), IAG and genetic algorithm based on Fuzzy Logic Controller (FLC) and experimental results show that there is an improvement on genetic algorithm performance based on FLC. Keywords: Genetic Algorithm, Fuzzy Logic Controller (FLC), Mutation Rate

A novel intelligent method for task scheduling in multiprocessor systems using genetic algorithm

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.

Background: This article reviews recent patents pertaining to the technological achievements and shortcomings of aircraft Position Indicator and Actuator System (PIAS). Generally, most patents have concentrated more on design, while implementing various developed single or multiobjective algorithms to solve component or system optimal design problems such as those of PIAS. Thus, there is a noticeable lack of patent efforts on either developing new constraint-handling techniques especially for multi-objective optimization, or applying advance single-objective constrainthandling techniques to multi-objective optimization. Method: To address this, the authors of this article in there past work developed a multi-objective optimization algorithm called Genetic Pareto Set Identification Algorithm plus Different Sex (GPSIA+DS), which considers the feasible and infeasible solution in it optimization strategy. However, the application of GPSIA+DS to real world reliability engineering problem has not been validated. Result: Thus, in this work, GPSIA+DS ability to handle an aircraft PIAS optimization problem was presented. PIAS component failure rates were obtained via Fault Tree Analysis (FTA). PIAS cost and reliability were the study objective functions subject to weight constrains. The performance of GPSIA+DS for the test problem was investigated through a comparative study. Conclusion: In the comparative study GPSIA+DS was compared with fast-sort genetic algorithm plus constrained dominance principle (NSGA-II+CD). Although NSGA-II+DS out performed GPSIA+DS for this test problem, it is evident that GPSIA+DS is equally an efficient algorithm. Keywords: Aircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliabilityAircraft system, constrain, failure rate, genetic algorithm, multi-objective optimization (MOO), pareto set, reliability

Sugarcane is one of important crops in Indonesia. In this paper, the sugarcane production, sugar industry development, sugar consumption, sugar import and export business and related management policies are all be summarized. In the meantime, the future prospect of the sugarcane industry is expounded.

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

The problem of defining optimal conditions for grinding plasma coatings may be considered as multi-objective optimization problem with a system of bounding inequalities that contain surface roughness, temperature, local and residual stresses as well as intrinsic defects size. This approach contributes to applying evolutionary algorithms such as genetic algorithm to solve the stated problem. Taking into account special characteristics of technological process, modification of the classical genetic algorithm has been carried out in the presented research. The combined method of selection based on the mitosis and meiosis operators makes it possible to increase fitness of a population ensuring its diversity during the following iterations. It is particularly important to maintain population diversity in genetic algorithm. The reason for that is preventing premature convergence which causes the obtained solution to be far from optimal. Another way to ensure population diversity is applying the developed mutation domain model that allows to alter random genes in chromosomes with the lowest value of the fitness function. The presented algorithm is based on both the combined method of selection and the mutation domain model. In order to compare the results of solving the problem of optimization of plasma coatings grinding process using modified genetic algorithm with other evolutionary algorithms, solutions performed by the classical genetic algorithm, ant colony optimization, particle swarm optimization and scatter search algorithm are presented. It was found that applying modified genetic algorithm provides high efficiency of solving process and reliability of the obtained results.

On the use of genetic algorithms for response surface modeling in high-performance liquid chromatography and their combination with the Microsoft Solver

A modified genetic algorithm for precise determination the geometrical orbital elements of binary stars

This work presents two new approaches to the optimum wells placement problem in oil fields using evolutionary methods. The presented approaches are a new space-efficient chromosome and the application of cellular genetic algorithms to guarantee healthy population diversity. Usually, authors define a domain representation having wells coordinates and types at any arbitrary position of the chromosome. On the other hand, the proposed representation enforces a unique relative wells position for each combination of wells, so, in general, there is only one problem domain representation for every concrete solution. Therefore, our representation reduces the search space, thus making the optimization more efficient. Furthermore, this paper employs a cellular genetic algorithm and presents a comparison between it and the classical genetic algorithm usually applied to this problem. The experiments with the UNISIM-I reservoir model indicate an enhancement of 6–10 times of the final \({\textit{NPV}}\) when comparing the proposed representation and the traditional one. Furthermore, the cellular genetic algorithm with the suggested chromosome performs better than the classical genetic algorithm by a factor of 1.5. These models are valuable not only for the oil and gas industry but also to every integer optimization problem that employs evolutionary algorithms.

Socio-economic impacts of Brazilian sugarcane industry

The article develops an effective method for solving optimization problems based on the synthesis of evolution ary mechanisms of nature’s development, which are based on the principles of genetic search for the best solu tions. It analyzes evolutionary concepts of biological species development by such well-known scientists as Ch. Darwin, J. Lamarck, Hugo de Vries, K. Popper, and M. Kimura. The key mechanisms for the emergence of new individuals with better adaptation (the best solutions) are identified. Based on the known relevant concepts, the main provisions of the theory of evolution are developed. Corresponding models are constructed, which in turn become computational analogies for the development of evolutionary methods for solving optimization prob lems. An optimization method has been developed based on a genetic algorithm, which is based on the basic operators of evolution: reproduction (selection), crossover, and mutation. A distinctive feature of the proposed approach is the hybridization of the classical genetic algorithm with adaptive mechanisms for parameter tuning and local improvement of solutions. The genetic algorithm used reproduction operators (tournament and roulette wheel selection), single- and double-point crossover, and mutation. This allows for an increase in the efficiency of global search in terms of convergence (number of computational iterations) and solution accuracy (average absolute error of the solution at 100 runs), as well as avoiding the stopping of the computational process at local extrema. Based on the developed optimization method, a genetic algorithm has been created that incorporates all the mechanisms of evolutionary computation. A genetic algorithm has been developed that contains all the mech anisms of evolutionary computation. Based on the genetic algorithm, model problems of multi-criteria optimi sation were calculated in Python in binary coding of the optimal solution (OneMax, LeadingOnes) and in coding of the solution using real numbers (two-extreme function). In the corresponding test problems, the stable achieve ment of the global extremum of the objective function and the stability of the algorithm were recorded. This allows us to conclude that the proposed method of optimizing multi-criteria functions based on genetic algo rithms is effective.

Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study