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.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a Hybrid intelligence Algorithm based on the binary Algorithm of particle swarm Optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm Optimization Algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed Algorithm compared to other Algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

Prediction of heart disease is very important because it is one of the causes of death around the world. More-over, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side e ects it. Ma-chine learning Algorithms are able to identify an e ective pattern for diagnosis and treatment of the disease and identify e ective factors in the disease. this paper is in-vestigated a new Hybrid Algorithm of Whale Optimiza-tion and Dragon y Algorithm using a machine learning Algorithm. the Hybrid Algorithm employs a Support Vec-tor Machine Algorithm for e ective Prediction of heart disease. Proposed method is evaluated by Cleveland standard heart disease dataset. The experimental re-sult indicates that the SVM accuracy of 88. 89 % and nine features are selected in this respect.