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.

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.

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.