A genetic algorithm for optimization of laminated dies manufacturing

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Niching ethods are useful extension of evolutionary algorithm that can permit population to search many peaks in parallel in multimodal domains. Traditional niching methods that can effectively be used to solve function optimization problems (FOPs) are not suitable for solving combinatorial optimization problems (COPs). We propose a new genetic algorithm (GA) for forming niches that can be efficiently applied to COPs. For this, we propose four requirements that are needed for an ideal method of forming niches for COPs. We then discuss a niching method we designed that satisfies these requirements. The proposed GA was applied to job shop scheduling problems (JSP) to demonstrate its effectiveness.

Genetic Algorithms (GAs) have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.

Genetic algorithms (GAs) are a stochastic search method for optimization problems based on the mechanics of natural selection and natural genetics (i.e., survival of the fittest). GAs have demonstrated considerable success in providing good solutions to many complex optimization problems and received more and more attentions during the past three decades. When the objective functions to be optimized in the optimization problems are multimodal or the search spaces are particularly irregular, algorithms need to be highly robust in order to avoid getting stuck at a local optimal solution. The advantage of GAs is just able to obtain the global optimal solution fairly. In addition, GAs do not require the specific mathematical analysis of optimization problems, which makes GAs themselves easily coded by users who are not necessarily good at mathematics and algorithms.KeywordsGenetic AlgorithmNash EquilibriumGenetic OperatorSoft ConstraintRoulette WheelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP). The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process. A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem. Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position. A min-max approach is based on minimum capacity and maximum capacity of vehicles. We analyzed the performance of the algorithms using evacuation datasets. The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms. Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.KeywordsDiscrete Particle Swarm OptimizationEvacuation ProcessEvolutionary ComputationGenetic AlgorithmVehicle Assignment Problem

In this paper the image compression problem is analyzed using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs the clustering efficiently. The possibility of solving clustering problems with genetic algorithms provides optimal solution. The present work makes use of genetic clustering algorithms to obtain an ordered representation of the image and then applies the DWT (Discrete Wavelet Transformation) to compress the image. The image quality is measured using PSNR measure. The genetic algorithm (GA)-based compression image has more PSNR value compared to the image without GA. It shows that the quality of the image is good for GA based compression. Also it reduces the problem of memory utilization efficiently due to compression and at the same time quality of the image is maintained due to GA. Keywords: Genetic algorithms, clustering, image compression, discrete wavelet transformation.

Since the era of big data, enhancement of the speed of the database query is the critical problem. Fundamental part of any database management system is query optimizer. So, it should be able to choose the suitable search strategy which can give quicker response even with complex and large amount of data. The expansion of database application areas, where a query may include more than 100 joins, traditional query optimization techniques, i.e., greedy-based optimization and dynamic programming unable to find the solution but its observed from the metaheuristic algorithm like genetic algorithms (GA), particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms gives optimal solution. These algorithms do not guarantee the best optimal solution, rather near best solution, which is tolerable. However, there is the limitation of slow convergence and high computation effort in GA and stagnation with premature convergence in ACO. Therefore, we proposed the hybridization of our modified ACO with GA to overcome the above limitations for multi-join query optimization and experimentally shown our proposed method on randomly created dataset gives better results over GA and ACO in terms of distance and execution times.

Static and adaptive distributed data replication using genetic algorithms

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

Biological-based genetic algorithms for optimized disaster response resource allocation

This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.

Over the years, rapid prototyping (RP) has been implemented in several companies in the manufacture of three-dimensional (3D) models. This process produces parts in a short time, in small quantities, and with complex geometry and is cheaper. RP can be divided into additive manufacturing and subtractive manufacturing, the former being the most used in 3D object printing. Additive manufacturing is a widely used process consisting of the adding of material layer by layer for building 3D objects from a model projected on a computer. This technology allows the printing of objects with complex shapes and is being increasingly adopted by the aircraft industry, jewelry, footwear, automotive, fashion products and medical implants, among others. When printing 3D objects, the build orientation optimization of 3D models influences the costs and surface quality. In this work, three build orientation optimization problems are studied: the single-objective problem, bi-objective problem and many-objective problem. To this end, three quality measures are applied: the support area, the build time and the surface roughness, for the car hoodvent model. First, a single-objective optimization problem is presented and solved by the genetic algorithm, obtaining optimal solutions for each objective function. Then, a study of the bi-objective optimization problem is carried out for each pair of objectives, and some representative trade-off solutions are identified. Finally, the study of the many-objective optimization problem, considering the three measures optimized simultaneously, is presented with some more optimal solutions found. The bi-objective and many-objective problems are solved by a multi-objective genetic algorithm. For a better analysis and comparison of the solutions found, Pareto fronts are used, enabling a better visualization of the solutions between the objectives. This chapter aims to assist the decision-maker in choosing the best part print orientation angles according to his/her preferences. The proposed approach found optimal solutions confirming its effectiveness.

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

In order to explain features of a point-to-point human arm movement, several models based on a minimization principle have been proposed. For example, there are the minimum hand jerk model, the minimum angle jerk model, the minimum commanded torque change model, the minimum-variance model and so on. For some models listed above; the minimum hand jerk model, the minimum angle jerk model and the minimum commanded torque change model, these established methods to obtain an optimal trajectory which satisfies corresponding criterion have already been reported. However it is difficult to obtain the optimal solutions of the minimum-variance model and of other some models. In this study, we propose a novel method to calculate optimal trajectories without depending on the employed criterion by using genetic algorithm. The proposed method has following two procedures; first, the objective trajectory is described by the sum of polynomial equations which satisfy the boundary conditions strictly, and second, the coefficients of the polynomial expressions are determined by using genetic algorithm. In this report, we have confirmed the effectiveness of our method in numerical experiments. First, in the minimum commanded torque change model, we show that the produced trajectory is in beautiful agreement with the optimal solution. Next, in the minimum-variance model, the obtained solution by using proposed method has better evaluated values than one of the quasi-optimal solution that is obtained by the conventional Simplex method

Solving TSP by Considering Processing Time: Meta-Heuristics and Fuzzy Approaches