hybrid algorithm

In this paper, three popular algorithms, including the Exchange Market Algorithm (EMA), the Shuffled Complex Evolution (SCE) algorithm, and the Queen Bee (QB) algorithm, are considered to propose three new hybrid evolutionary algorithms named EMA-QB, EMA-SCE, and EMA-SCE-QB. Then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of EMA, SCE, and QB algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. It is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. Finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms.

One of the solutions to deal with improper temporal and spatial distribution of water resources problems is the optimal use of dam reservoirs. Reservoir systems analyze, properly, manages and properly utilizes water resources and tries to design water resources systems in such a way that specific purpose such as hydropower generation, drinking water supply and agriculture, control of destructive floods, according to a set of Implement the constraints optimally. For this purpose, in this study, the objective function of minimizing the total power of the difference between the demand of agriculture and release has been used to solve the problem of optimizing the operation of the Amirkabir reservoir. The purpose of this study was to evaluate the performance of single-objective versions of algorithms such as multi-verse optimizer and genetic algorithm, as well as the performance of a combination of these two algorithms (MVGA). The results of the study of meta-heuristic algorithms indicated that among the multi-verse, genetic algorithm and MVGA algorithm, the MVGA algorithm similar to GA has a lower number of iterations with objective function values of 24.29 and 24.22, respectively, better than the MVO algorithm with objective function values 29.14. The results of this study showed that to increase the efficiency of one algorithm, it can be combined with another algorithm. In this study, the combination of genetic algorithm with multi-world algorithm has improved the performance of multi-world algorithm by 16.64%.

The goal of production planning in a refinery is to produce as many valuable products as possible such as gasoline, jet fuel, diesel, etc., while meeting market demand and other constraints. Crude oil refining is one of the most complex chemical industries; Therefore, optimizing the production planning of an oil refinery is considered as one of the most difficult and challenging issues in this field. Due to rapid changes in industry-related technologies such as the construction of new catalysts, the design of more flexible process units, the flexibility of refineries is increasing rapidly. With the flexibility of refineries, their production planning requires a mathematical model that can be used to make the best decision at the right time to meet market demand at the lowest production cost. In this paper, the production planning of a flexible refinery is modeled using mathematical relationships between macro parameters such as product demand, production conditions, fixed and variable production costs, and inventory costs of petroleum products. To solve it, a hybrid algorithm of combining genetic algorithm and fix and optimize is proposed. The results with using of 63 simulated problems in small, medium and large dimensions show that the near-optimal solution obtained from the hybrid method deviates 0.23 and 0.12 percent of the exact solution of the problem on average in small and medium dimensions respectively. Also in large dimensions where it was not possible to calculate the exact answer by the computer, this algorithm can answer in an average of 87 seconds.

Nowadays, the Hadoop open-source project with the MapReduce framework has become very popular as it processes vast amounts of data in parallel on large clusters of commodity hardware in a reliable and     fault-tolerant manner. MapReduce was introduced to solve large-data computational problems, and is    dependent on the divide and conquer principle. Time and scheduling are always the most important aspects, hence in the past decades in the MapReduce environment, many scheduling algorithms have been proposed. The main ideas of these algorithms are increasing data locality rate, and decreasing response time and completion time. In this research we have proposed a new hybrid scheduling algorithm (HSMRPL) which uses dynamic job priority and identity localization techniques, and focuses on increasing data locality rate and decreasing completion time. We have evaluated and compared our algorithm with hadoop default schedulers by running concurrent workloads consisting of the WordCount and Terasort benchmarks. The results show that our proposed algorithm has increased the localization rate by 10.4% and 18.5% and the speed by 3.14% and 3.3% compared to the FIFO algorithm and the Fair algorithm respectively.

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.

This study proposes a new hybrid algorithm for optimum design of skeletal structures using a search method inspired by such meta-heuristic algorithms as GA, CSS, and PSO. In the proposed method, the exploration space is searched via moving points. Each design in the design space corresponds to a moving point in the exploration space. Collectively, these moving points form a population. By moving within the exploration space, these moving points create an evolutionary process for successive populations while moving towards the optimal point. The movement and displacement of the moving points in the exploration space is consistently based on the factors and characteristics of the previous population. To this end, similar to other meta-heuristic algorithms, the first population is created randomly. Then, the positions of the points in the next population are determined based on the geometric center of the previous population, the geometric center of the selected points, and the positions of

the selected points in the previous population. In this way, the points form a new population by moving within the design space towards the collective center of the points, the collective center of the selected points, and the respective positions of the selected points in the previous population. The average quality of the present population points, the average quality of the set of the selected points, and the quality of each selected point affect the displacement of the moving points. Other significant factors affecting the formation of points in a new population include variation as well as displacement of individual points in the previous population, acting independent parameters in specifying a new position for each point. To evaluate the efficiency of the proposed algorithm, we used a number of the benchmark examples. To this end, we plotted the optimization process convergence diagram for each example to study the method used in the proposed algorithm for obtaining the optimum point. On the other hand, we determined the average number of successive runs obtained for the proposed algorithm for each example. Our results showed that the best and the average run convergence trends calculated for different examples were in good agreement, which is a sufficient proof that the proposed algorithm possesses the required efficiency in obtaining the optimum point.