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.

Terrain simplification problem is one of fundamental problems in computational geometry and it has many applications in other fields such as geometric information systems, computer graphics, image processing. Terrain is commonly defined by a set of n points in three dimension space. Major goal of terrain simplification problem is removing some points of one terrain so that maximum error of simplified surface is a certain threshold. There are two optimization goals for this problem: (1) min-k, where for a given error threshold e, the goal is to find a simplification with the minimum number of points for which the error is that most e, and (2) min-e, where for a given number n, the goal is to find a simplification of at most m points that has the minimum simplification error. Simplification problem is NP-hard in optimal case.In this paper we present a HYBRID ALGORITHM for terrain simplification that performs in three phases. First, terrain is divided to some clusters, then any cluster is simplified independently and finally, the simplified clusters are merged. Our ALGORITHM solves the problem in O (n2Ön). The proposed ALGORITHM is implemented and verified by experiments.

Bitcoin and digital currencies have emerged as a new market for investment. Therefore, the prediction of their future trend and prices is highly significant. In this research, the factors influencing the price of bitcoin were identified and extracted based on previous researches. The identified factors include the US dollar index, CPI index, S and P 500, Dow Jones, and gold price. Considering the performance of metaheuristic ALGORITHMs in predicting bitcoin price, this research utilized genetic ALGORITHM and particle swarm optimization ALGORITHM, and proposed a HYBRID ALGORITHM to improve their performance.According to our results, among the investigated factors, the US dollar index has the greatest impact on bitcoin price, followed by inflation rate and the CPI index. Additionally, the proposed HYBRID ALGORITHM outperforms the particle swarm optimization and genetic ALGORITHMs, with a prediction error of 7.3%. It should be noted that the type and magnitude of the impact of the investigated factors may change over time. For example, a factor that previously had a direct impact may become reversed or neutralized over time.

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.

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.

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering ALGORITHMs in many fields, a lot of research is still going on to find the best and efficient clustering ALGORITHM to partition the data items. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper, a new HYBRID data clustering approach which combines the modified krill herd and K-means ALGORITHMs, named as K-MKH, is proposed. K-MKH ALGORITHM utilizes the power of quick convergence behaviour of K-means and efficient global exploration of Krill Herd and random phenomenon of Levy flight method. The Krill-herd ALGORITHM is modified by incorporating Levy flight into it to improve the global exploration. The proposed ALGORITHM is tested on artificial and real life datasets. The simulation results are compared with other methods such as K-means, Particle Swarm Optimization (PSO), Original Krill Herd (KH), HYBRID K-means and KH. Also the proposed ALGORITHM is compared with other evolutionary ALGORITHMs such as HYBRID modified cohort intelligence and K-means (K-MCI), Simulated Annealing (SA), Ant Colony Optimization (ACO), Genetic ALGORITHM (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means++. The comparison shows that the proposed ALGORITHM improves the clustering results and has high convergence speed.

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing total cost of the objective function with technical constraints and reliability limits, make it a combinatorial problem which should be solved by optimization ALGORITHMs. This paper presents a new HYBRID simulated annealing and tabu search ALGORITHM for distribution network expansion problem. Proposed HYBRID ALGORITHM is based on tabu search and an auxiliary simulated annealing ALGORITHM controls the tabu list of the main ALGORITHM. Also, another auxiliary simulated annealing based ALGORITHM has been added to local searches of the main ALGORITHM to make it more efficient. The numerical results show that the method is very accurate and fast comparing with the other ALGORITHMs.