Nesting of Two-Dimensional Irregular Parts Within an Irregular Boundary Using Genetic Algorithm

An integrated machine vision based system for solving the nonconvex cutting stock problem using genetic algorithms

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

In this paper, a mathematical model is established for the three-dimensional packing problem. This model is a multi-objective optimization problem which considers two factors: volume utilization ratio and load utilization ratio. This paper uses heuristic algorithm to design on the basis of the actual experience of packing. At the same time, combining genetic algorithm and heuristic algorithm, a three-dimensional hybrid genetic heuristic algorithm (3D-HGH) is proposed. This algorithm improves the performance of heuristic algorithm and improves the utilization ratio. The results of three-dimensional visualization of loading are obtained by simulation experiments.

First, we prove the decomposition theorem for the regularities of multifractal Hausdorff measure and packing measure in ”d. This decomposition theorem enables us to split a set into regular and irregular parts, so that we can analyze each separately, and recombine them without affecting density properties. Next, we give some properties related to multifractal Hausdorff and packing densities. Finally, we extend the density theorem in [6] to any measurable set.

Публикация посвящена анализу динамики численности и характера формирования воинских гарнизонов колывано-воскресенских предприятий во второй четверти XVIII в. в контексте регионального освоенческого и административно-политического процессов. Источниковую основу исследования составили подавляющей частью не введенные в научный оборот и во многом уникальные массивы трех фондов Российского государственного архива древних актов.
 Автор приходит к выводу о том, что на протяжении второй четверти XVIII в. шло планомерное увеличение численности гарнизонов Колывано-Воскресенских заводов, состоявших как из регулярных, так и иррегулярных частей. Вопрос охраны производств регулировался в рамках управленческого треугольника «гражданские власти – частновладельческие, а затем ведомственные кабинетские структуры – военные власти», где воеводы Кузнецкого уезда односторонне и малорезультативно стремились сохранить свое административное влияние на складывавшееся горное ведомство, а динамика насыщения воинскими подразделениями увязывалась не столько с расширением значимости производственного комплекса, сколько становилась реакцией на военные акции кочевников в приграничной зоне.

In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP) model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA), and three heuristic algorithms (Johnson, SPTCH and Palmer) are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.

Multistage interconnection networks (MINs) are popular in switching and communication applications and have been used in telecommunication and parallel computing systems for many years. Crosstalk a major problem introduced by an optical MIN, is caused by coupling two signals within a switching element. We focus on an efficient solution to avoiding crosstalk by routing traffic through an N X N optical network to avoid coupling two signals within each switching element using wavelength-division multiplexing (WDM) and a time-division approach. Under the constraint of avoiding crosstalk, the interest is on realizing a permutation that uses the minimum number of passes for routing. This routing problem is an NP-hard problem. Many heuristic algorithms are already designed by researchers to perform this routing such as a sequential algorithm, a degree-descending algorithm, etc. The genetic algorithm is used successfully to improve the performance over the heuristic algorithms. The drawback of the genetic algorithm is its long running times. We use the simulated annealing algorithm to improve the performance of solving the problem and optimizing the result. In addition, a wavelength lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using heuristic, genetic, and simulated annealing algorithms. Many cases are tested and the results are compared to the results of other algorithms to show the advantages of simulated annealing algorithm.

A relax-and-fix Pareto-based algorithm for a bi-objective vaccine distribution network considering a mix-and-match strategy in pandemics

Genetic algorithms have demonstrated considerable success in providing good solutions to many NP-hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small toy problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the NP-hard problem of multiple fault diagnosis (MFD). We compare a pure genetic algorithm with several variants that include local improvement operators. These operators, which are often domain-specific, are used to accelerate the genetic algorithm in converging on optimal solutions. Our empirical results indicate that by using the appropriate local improvement operator, the genetic algorithm is able to find an optimal solution in all but a tiny fraction of the cases and at a speed orders of magnitude faster than exact algorithms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A hidden Markov model (HMM) is a natural and highly robust statistical method for automatic speech recognition. It has been tested and proved effective in a wide range of applications. The HMM model parameters are used to describe the utterance of the speech segment presented by the HMM. Many successful heuristic algorithms are developed to optimize the model parameters to best describe the training observation sequences. However, all these methods are exploring for only one local maximum in practice. No single method can be recovered from the local maximum and to obtain the global maximum or other more optimized local maxima. In this paper, a stochastic search method called the genetic algorithm (GA) is presented for HMM training. GA mimics natural evolution and performs searching within the defined searching space. Experimental results showed that using GA for HMM training (GA-HMM training) can obtain better solutions than using heuristic algorithms. One of the major drawbacks is that GAs require a lot of computation power for global searching before it can converge. Therefore, in order to outperform heuristic algorithms, a parallel version of GA called the parallel genetic algorithm (PGA) is introduced. Experimental results showed that using PGA in speech recognition systems provides 18% improvement in recognition rate with the same amount of computational time.

Aligning multiple DNA or protein sequences is a fundamental step in the analyses of phylogeny, homology and molecular structure. Heuristic algorithms are applied because optimal multiple sequence alignment is prohibitively expensive. Heuristic alignment algorithms represent a practical trade-off between speed and accuracy, but they can be improved. We present EVALYN (EVolved ALYNments), a novel approach to multiple sequence alignment in which sequences are progressively aligned based on a guide tree optimized by a genetic algorithm. We hypothesize that a genetic algorithm can find better guide trees than traditional, deterministic clustering algorithms. We compare our novel evolutionary approach to CLUSTAL W and find that EVALYN performs consistently and significantly better as measured by a common alignment scoring technique. Additionally, we hypothesize that evolutionary guide tree optimization is inherently efficient and has less time complexity than the commonly-used neighbor-joining algorithm. We present a compelling analysis in support of this scalability hypothesis.

In this paper, we present a hierarchicity-based (self-similar) hybrid genetic algorithm for the solution of the grey pattern quadratic assignment problem. This is a novel hybrid genetic search-based heuristic algorithm with the original, hierarchical architecture and it is in connection with what is known as self-similarity—this means that an object (in our case, algorithm) is exactly or approximately similar to constituent parts of itself. The two main aspects of the proposed algorithm are the following: (1) the hierarchical (self-similar) structure of the genetic algorithm itself, and (2) the hierarchical (self-similar) form of the iterated tabu search algorithm, which is integrated into the genetic algorithm as an efficient local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm.

This paper presents a Pareto-based optimization algorithm to determine a series of best part orientations in stereolithography systems. Previous methods could apply limit objective functions (OFs). Moreover, methods with multiple OFs had abstracted them into a single fitness function. A single fitness function never reflects the characteristics of individual OFs properly. The method proposed here handles several OFs individually. The objective functions are the build time and support volume under a desired surface finish. The optimization was performed using the multi-objective genetic algorithm (MOGA). At each genetic algorithm step, the surface finish was achieved by applying the adaptive layer thickness method. Pareto-based optimization finds a series of best part orientations with minimum build time and volume support. It is a multi-objective optimization that handles complex CAD files. The algorithm was developed using MATLAB. The codes were run for some case studies and the results were very promising.

The complexity of embedded system design increase as the technology keeps evolving from day to day. Hardware software partitioning has been a promising approach to solve this design problem of complexity in the embedded systems, by providing a solution that automatically decides the partitioning. A lot of research has been done to automate the partitioning which focusing on exact and heuristic algorithm. Then due to the slow performance of the exact algorithms, the study focus shift to heuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this research the performance of both PSO algorithm and GA are analyzed in the application of the partitioning. In order to get the best among these two algorithms, hybrid combination across the two algorithms is designed. The best cost and their average time to achieve it are compared among PSO, GA and hybrid design. As a result, the graph obtained from the hybrid GA-GA-PSO required a smaller number of iterations to reach best cost. Compared to previous work, GA-GA-PSO obtained a smooth as the successive PSO graph. In conclusion, a new idea of hybrid across PSO and GA has been introduced and it results into a better solution for Hardware Software Partitioning.

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.