Search results for "Genetic algorithm"
showing 10 items of 834 documents
Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process
2015
In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the …
Optimal Set Points Regulation of Distributed Generation Units in Micro-grids under Islanded Operation
2010
The present work studies the problem of optimizing the power production levels of dispersed generation units in islanded microgrids. The problem is intrinsically multi-objective with non linear objectives and constraints, thus the solution approach is based on evolutionary optimization and uses the Non dominated Sorting Genetic Algorithm II. The objectives are calculated based on the solution of the load flow problem. The latter problem is more complicated when in the considered system a physical node with a sufficiently large production capability is not available, because all the generation node of the systems have similar and limited generation capability. In this paper, the issue has be…
Biased Modern Heuristics for the OCST Problem
2011
Biasing modern heuristics is an appropriate possibility in designing problem-specific and high-quality modern heuristics. If we have knowledge about a problem we can bias the design elements of modern heuristics, namely the representation and search operator, fitness function, the initial solution, or even the search strategy. This chapter presents a case study on how the performance of modern heuristics can be increased by biasing the design elements towards high-quality solutions. Results show that problem-specific and biased modern heuristics outperform standard variants and even for large problem instances high-quality solutions can be found.
Multipass machining optimization by using fuzzy possibilistic programming and genetic algorithms
1999
The paper deals with optimal determination of the cutting parameters in multipass machining operations. A new optimization approach is proposed which uses a possibilistic formulation of the classical optimization problem and optimizes the resulting possibilistic model using a genetic algorithm. The proposed approach makes it possible to find the optimal value of all the cutting parameters, including the depth of cut, in just one step. A numerical example is provided to compare the performance of the proposed method with other recent methods proposed in the literature. Furthermore, fuzzy data must be used in the formulation of the optimization problem and therefore a fuzzy possibilistic app…
Solving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertainty
2019
In this work, a mixed-integer binary non-linear two-echelon inventory problem is formulated for a vendor-buyer supply chain network in which lead times are constant and the demands of buyers follow a normal distribution. In this formulation, the problem is a combination of an (r, Q) and periodic review policies based on which an order of size Q is placed by a buyer in each fixed period once his/her on hand inventory reaches the reorder point r in that period. The constraints are the vendors’ warehouse spaces, production restrictions, and total budget. The aim is to find the optimal order quantities of the buyers placed for each vendor in each period alongside the optimal placement of the ve…
A double genetic algorithm for the MRCPSP/max
2011
This paper presents a heuristic solution procedure for a very general resource-constrained project scheduling problem. Here, multiple execution modes are available for the individual activities of the project. In addition, minimum as well as maximum time lags between different activities may be given. The objective is to determine a mode and a start time for each activity such that the temporal and resource constraints are met and the project duration is minimised. Project scheduling problems of this type occur e.g. in process industries. The heuristic is a two-phased genetic algorithm with different representation, fitness, crossover operator, etc., in each of them. One of the contribution…
Tabu search and GRASP for the maximum diversity problem
2007
In this paper, we develop new heuristic procedures for the maximum diversity problem (MDP). This NP-hard problem has a significant number of practical applications such as environmental balance, telecommunication services or genetic engineering. The proposed algorithm is based on the tabu search methodology and incorporates memory structures for both construction and improvement. Although proposed in seminal tabu search papers, memory-based constructions have often been implemented in naive ways that disregard important elements of the fundamental tabu search proposals. We will compare our tabu search construction with a memory-less design and with previous algorithms recently developed for…
Hydropower Optimization Using Split-Window, Meta-Heuristic and Genetic Algorithms
2019
In this paper, we try to find the most efficient optimization algorithm that can be used to resolve the hydropower optimization problem. We propose a novel optimization technique is called the Split-window method. The method is relatively simple and reduces the complexity of the optimization problem by split-ting the planning horizon (and datasets) into equal windows and assigning the same values to policies(actions) within each part. After splitting, a meta-heuristic technique is used to optimize the actions, and the dataset is split again until a split contains only one instance (timestep). The unique values to be optimized during each iteration is equal to the number of splits which make…
Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography
2009
In this article, various metaheuristics for a numerical optimization problem with application to Electric Impedance Tomography are tested and compared. The experimental setup is composed of a real valued Genetic Algorithm, the Differential Evolution, a self adaptive Differential Evolution recently proposed in literature, and two novel Memetic Algorithms designed for the problem under study. The two proposed algorithms employ different algorithmic philosophies in the field of Memetic Computing. The first algorithm integrates a local search into the operations of the offspring generation, while the second algorithm applies a local search to individuals already generated in the spirit of life-…
Developing Domain-Knowledge Evolutionary Algorithms for Network-on-Chip Application Mapping
2013
This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Chip application mapping Evolutionary Algorithms are developed, evaluated and optimized for minimizing the NoC communication energy. Two crossover and one mutation operators are proposed. It is analyzed how each optimization algorithm performs with every genetic operator, in terms of solution quality and convergence speed. Our proposed operators are compared with state-of-the-art genetic operators for permutation problems. Finally, the problem is approached in a multi-objective w…