Hybrid Algorithm

The hybridization of heuristics methods aims at exploring the synergies among stand alone heuristics in order to achieve better results for the optimization problem under study. In this paper we present a hybridization of Genetic Algorithms (GAs) and Tabu Search (TS) for scheduling in computational grids. The purpose in this hybridization is to benefit the exploration of the solution space by a population of individuals with the exploitation of solutions through a smart search of the TS. Our GA (TS) hybrid algorithm runs the GA as ...

Chemical process control requires intelligent monitoring due to the dynamic nature of the chemical reactions and the non-linear functional relationship between the input and output variables involved. CSTR is one of the major processing unit in many chemical, pharmaceutical and petroleum industries as well as in environmental and waste management engineering. In spite of continuing advances in optimal solution techniques for optimization and control problems, many of such problems remain too complex to be solved by the known techniques. Thus, a heuristic approach is often a viable alternative. Neural Network models offer the most unified approach to building truly intelligent systems, which can provide good optimal solution for many applications. In this work we propose a hybrid (KohKal) neural network algorithm which is being used to model and solve a continuous stirred tank mixer/reactor (CSTM/R) problem which is non-linear and stochastic in nature. This hybrid algorithm is robust...

Structural optimization of Lennard-Jones (LJ) clusters is a classical NP problem. There are many local minima locating near the global minimum, and the local optima number is increased exponentially. Social cognitive optimization algorithm (SCOA) is a new swarm intelligent technique by simulating the human society. However, its local search capability is still weak. Therefore, in this paper, a novel local search strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is employed to enhance the exploitation capability of SCOA. Simulation results show the proposed hybrid algorithm has successfully found the lowest-energy structures of the LJ2-LJ5, LJ7-LJ9 and LJ12 comparing with PSO and the standard SCOA.

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers. The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.

Abstract In this paper, an investigation of the behavior of a recently defined hybrid algorithm for continuous variables electromagnetic optimization problems is presented. This algorithm makes use of the nonlinear simplex method as a principal optimizator, and a greedy genetic algorithm to explore the search space. The algorithm is greatly affected by the tuning of some parameters. Referring to a solenoids system optimal design problem, a detailed comparison of performances obtained implementing a metaheuristic tuning is presented

This abstract sketches the approach and the employed means for constructing measures of melodic similarity in our software toolbox SIMILE. For different tasks related to melodic similarity different hybrid algorithms proved to be optimal. The hybrid algorithms consist of several single algorithms mostly known in the literature that are taken together in a linear combination. One special linear combination is chosen to serve for the given task in this contest. The single measures contained in this hybrid algorithm are explained briefly and references to more detailed publications are given.

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms’ strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.

This article introduces the sparse group fused lasso (SGFL) as a statistical framework for segmenting sparse regression models with multivariate time series. To compute solutions of the SGFL, a nonsmooth and nonseparable convex program, we develop a hybrid optimization method that is fast, requires no tuning parameter selection, and is guaranteed to converge to a global minimizer. In numerical experiments, the hybrid method compares favorably to state-of-the-art techniques with respect to computation time and numerical accuracy; benefits are particularly substantial in high dimension. The method's statistical performance is satisfactory in recovering nonzero regression coefficients and excellent in change point detection. An application to air quality data is presented. The hybrid method is implemented in the R package sparseGFL available on the author's Github page.

Heart disease is one of the most serious health threat growing among worldwide, for which mortality rate around the world is very high. Early detection of heart disease could save many lives, accurate detection of heart disease is crucial among the health care persons through regular clinical data and its analysis. Artificial intelligence is the effective solution for decision making and accurate heart disease predictions. Medical industry showing enormous development in using information technology, in which artificial intelligence play major role. In the proposed work, deep learning based approach on heart disease is done on Cleveland dataset. However existing studies are handled in Machine learning technique. The proposed work detects heart disease based in Convolutional Neural Networks. Experimental results shows our proposed work achieves high level of accuracy in prediction of heart disease.

Most of the existing CPU scheduling algorithms are not applicable in real-time environment due to major limitations like high in turnaround time, waiting time, and response time, also high context switches and less throughput. Our main aim is to provide a new hybrid CPU Scheduling algorithm that can enhance the CPU performance in a real-time environment. We have designed hybrid version of algorithm by integrating Highest Response Ratio Next (HRRN) and standard deviation (SD) scheduling algorithms. It combines the strengths of standard deviation, elimination of starvation, and HRRN priority scheduling. It assigns different priorities to processes thereby dealing with aging issue. Thus, our algorithm corrects major limitations of the existing CPU scheduling algorithms. We'll also provided a comparative analysis of our proposed new hybrid algorithm with the prevailing scheduling algorithms based on average turnaround, average waiting time, varying time quantum, and many context switches.

In order to coordinate the scheduling problem between an isolated microgrid (IMG) and electric vehicle battery swapping stations (BSSs) in multi-stakeholder scenarios, a new bi-level optimal scheduling model is proposed for promoting the participation of BSSs in regulating the IMG economic operation. In this model, the upper-level sub-problem is formulated to minimize the IMG net costs, while the lower-level aims to maximize the profits of the BSS under real-time pricing environments determined by demand responses in the upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA, is put forward by combining a real/integer-coded JAYA algorithm and the branch and bound algorithm (BBA), in which the JAYA and BBA are respectively employed to address the upper- and lower- level sub-problems, and the bi-level model is eventually solved through alternate iterations between the two levels. The simulation results on a microgrid test system verify the effectiveness and superiority of the presented approach.

Intrusion detection system (IDS) is an important tool for the defense of a network against attacks. It monitors the activities occurring in a computer system or network and analyzes them for recognizing intrusions to protect the computer network. Most of the existing IDSs use all of the 41 features available in the network packet to analyze and look for intrusive

Many scientific applications can benejit from eficient clustering algorithm of massively large high dimensional datasets. However most of the developed ,algorithms are impractical to use when the amount of data is very large. Given N objects each de3ned by an M-dimensional fea- ture vectol; any clustering technique for handling very large datasets in high dimensional space should run in time

Sentence alignment task in machine translation gained more importance recently. It is the task of finding correspondences of sentences in one language (eg. English) and another (eg. Malayalam, an Indian language). Basically, alignment aims to succeed the task of extracting structural information and statistical parameters from bilingual corpora. This process might seem very easy at first sight but it has some important challenges to face which make the task difficult. These challenges are discussed with special reference to English to Malayalam sentence alignment. There is a special mention on the previous work in this area and a new algorithm is proposed for sentence alignment in English to Malayalam.

In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching solution neighbors to improve the quality of global solutions. This part of the algorithm is used to search sparsely populated areas for finding the dominant solutions. To improve the algorithm, for each firefly some changes have been made on the criteria of determining the global optimal solution and doing local optimal solution; this leads to more uniformity of the Pareto curve and error reduction, as the experimental results show. The proposed algorithm is an extension of a basic algorithm.

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-ICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.

Chemical process control requires intelligent monitoring due to the dynamic nature of the chemical reactions and the non-linear functional relationship between the input and output variables involved. CSTR is one of the major processing unit in many chemical, pharmaceutical and petroleum industries as well as in environmental and waste management engineering. In spite of continuing advances in optimal solution techniques

In this article, we modeled the economic impacts of congestion pricing policies on micro and macro levels of the economy. On the consumer side of the market, we ran a discrete choice model and examined how travelers behave differently in response to higher driving costs caused by road congestion. While on the macro level, we defined the congestion pricing policy as an exogenous shock on economy and simulated the impacts of this stimulator on several sectors of economy.
Results for the case study of the Greater New York Metropolitan area shows that a congestion pricing policy on three designated freeways, with a daily flow over 3.5 million vehicles will induce around $0.55 bln increase in GDP. This growth is caused not only by the expansion of value added items, but also by the direct impact on GDP due to less fuel consumption and consequently less oil imports.