Numerous Algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a Hybrid Optimization method known as a PSOHHO Optimization Algorithm is presented. There are two methods for combining Algorithms: parallel and sequential. We adopted the parallel method and optimized the Algorithm's performance. We cover the weaknesses of one Algorithm with the strengths of another Algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two Algorithms, and a new population is created. With this ability, the strengths of an Algorithm can be used to compensate for the weaknesses of the other Algorithm. In this method, no changes are made to the Algorithms. The main goal is to use existing Algorithms. This method aims to attain the optimal solution in the shortest time possible. Two Algorithms of particle swarm Optimization (PSO) and Harris Hawks Optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic Algorithms.

Particle swarm Optimization (PSO) is one of the practical metaheuristic Algorithms which is applied for numerical global Optimization. It bene ts from the nature inspired swarm intelligence, but it su ers from a local optima problem. Recently, another nature inspired metaheuristic called Symbiotic Organisms Search (SOS) is proposed, which doesn't have any parameters to set at start. In this paper, the PSO and SOS Algorithms are combined to produce a new Hybrid metaheuristic Algorithm for the global Optimization problem, called PSOS. In this Algorithm, a minimum number of the parameters are applied which prevent the trapping in local solutions and increase the success rate, and also the SOS interaction phases are modi ed. The proposed Algorithm consists of the PSO and the SOS phases. The PSO phase gets the experiences for each appropriate solution and checks the neighbors for a better solution, and the SOS phase bene ts from the gained experiences and performs symbiotic interaction update phases. Extensive experimental results showed that the PSOS outperforms both the PSO and SOS Algorithms in terms of the convergence and success rates.

This paper presents a Hybrid Optimization Algorithm based on a modified genetic Algorithm (GA). The Algorithm includes two stages. In the first stage, a global search is carried out over the design search space using a modified GA. In the second stage, a local search is executed that is based on GA solution using a discretized form of Hooke and Jeeves method. The modifications on basic GA includes dynamically changing the population size Throughout the GA process, utilizing variable penalty multiplier and the use of a square root form of the penalty function in constraint handling. The Hybrid Algorithm and the modifications to the basic GA are examined on the design Optimization of a well-known test problem (10 bar truss). The effect of different parameters and techniques of handling GA operators on the performance of the proposed Algorithm is investigated. The Hybrid Algorithm is employed for the design Optimization of a reinforced concrete flat slab building  and the results are compared with those of using the GA only.      

In this paper, a new and an effective combination of two metaheuristic Algorithms, namely Firefly Algorithm and the Differential evolution, has been proposed. This Hybridization called as HFADE, consists of two phases of Differential Evolution (DE) and Firefly Algorithm (FA). Firefly Algorithm is the nature-inspired Algorithm which has its roots in the light intensity attraction process of firefly in the nature. Differential evolution is an Evolutionary Algorithm that uses the evolutionary operators like selection, recombination and mutation. FA and DE together are effective and powerful Algorithms but FA Algorithm depends on random directions for search which led into retardation in finding the best solution and DE needs more iteration to find proper solution. As a result, this proposed method has been designed to cover each Algorithm deficiencies so as to make them more suitable for Optimization in real world domain. To obtain the required results, the experiment on a set of benchmark functions was performed and findings showed that HFADE is a more preferable and effective method in solving the high-dimensional functions.

In this paper, a new Hybrid Particle Swarm Optimization (PSO) and Harmony Search (HS) Algorithm, denoted by PSOHS is presented. This Hybrid Algorithm is designed to improve the efficiency of the PSO and remove some of the disadvantages which reduce the capability of the PSO. The main problem of the PSO is the lack of balance between exploration and exploitation of the Algorithm. Another problem is how to handle the violating particles from feasible search space without reduction in the performance of the Algorithm. The problem of unbalanced exploration and exploitation is solved using linear varying inertia weight. The second problem is solved in some other Algorithms via reproduction of the violating particles using the HS Algorithm. In this paper, these two approaches are combined to achieve a more efficient Algorithm for engineering design problems. To show the higher capability of this approach compared to other works, several benchmark engineering examples, which have been considered previously and solved utilizing a variety of Optimization Algorithms, is solved by the present Hybrid Algorithm. Results illustrate a desirable performance of the PSOHS in both obtaining lower weight and having a higher convergence rate.