metaheuristic algorithms

Soft computing techniques in engineering sciences have covered a large amount of research. Among them is the design and optimization of navigation systems for use in land, sea, and air transportation systems. Therefore, in this paper, an attempt is made to take advantage of novel approaches of intelligent metaheuristic optimization for designing integrated navigation systems. For this purpose, the inclined planes system optimization algorithm with several modified and new versions have been used along with two well-known methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS inertia measurement modules. Process and measurement noise covariance matrices are considered as design variables and the sum of mean-squares-error as an objective function in the form of a single-objective minimization problem. Outputs are presented in terms of statistical and performance indicators such as runtime, fitness, convergences, angular-velocity accuracy, latitude, longitude, altitude, roll, pitch, yaw, and routing along with the ranking of algorithms. The overall assessment indicated the correctness of the performance and the relative superiority of the IPO and IIPO over the competitors and competitive performance of the assumed algorithms in comparison with the volume of considerations and calculations of the base problem.

Sparse matrices appear in a number of problems related to science and engineering. The performance of algorithms designed for solving such problems depends significantly on the bandwidth of the problem matrix.  The bandwidth of a sparse symmetric matrix is the distance from the main diagonal beyond which all elements of the matrix are zero. Minimizing the bandwidth of a matrix is an NP-complete problem. Considering the importance of this problem, numerous algorithms have so far been presented for its solution among which metaheuristic algorithms have performed much better than other algorithms. The issue with using metaheuristic algorithms for addressing this problem is that the bandwidth size, which has been employed for comparing the quality of solutions produced by these algorithms in almost all previous studies, is not an appropriate measure, and therefore cannot direct the search process towards high-quality solutions. In this research, the above-mentioned issue is investigated, and a new approach is presented for dealing with it.

Charging stations are one of the most important pieces of equipment for electric vehicles (EV). One of the most important challenges of charging stations is their optimal location, which practically puts the operation of the system components in the maximum area. Distribution networks are the final link in the electricity supply chain for consumers. Therefore, the economic and technical efficiency of these networks guarantees a stable and secure future in the electricity industry. In this regard, it is very important to study the role of EVstations. This paper investigates, the optimal location of charging and discharging stations and the optimal operation planning of Evs in a distribution network. The effective factors in choosing the location and the optimal charging and discharging rate in the stations are a combination of technical and economic issues. Regarding technical issues, the minimization of losses, the minimization of voltage drop in feeders and the uniformity of the network load curve were considered. In the economic field, the stations were located and the charging and discharging rates were determined in such a way that the charge and discharge costs in the stations and the total cost paid for the purchase of power were minimized as much as possible. In order To manage the load on the consumer side and to unify the load curve, the price-based demand response program was considered and implemented in the simulations.To find the optimal working point, genetic metaheuristic algorithms, genetic combination-particle swarm and genetic combination-colonial competition were used. All simulations were performed in MATLAB software To evaluate the proposed methods, validation was performed in each part on the IEEE standard testing system with a bus number of 69.

 The complexity of mathematical models, exponential growth of the solution time for many methods, lack of access to gradient information and optimal local convergence are some of the problems that optimal classical algorithms face in solving complex problems. In order to eliminate these drawbacks, metaheuristic algorithms are widely used to solve complex and multivariate problems. Choosing the best and most suitable algorithm is difficult due to their high diversity. In previous studies, some of these methods have been summarized, but due to overpublicize of these methods in recent years, there is no specific article to describe and compare all of these methods. In this paper, the most important metaheuristic optimization algorithms are introduced from 2012 till now. In separate sections for each algorithm, the history, source of inspiration, objective function and number of its setting parameters are stated. These algorithms are then categorized and compared using several theories. Due to the type of application of each algorithm in engineering problems, it is not possible to introduce a single algorithm as the best methodology, but the Gray Wolf Optimization (GWO) algorithm is one of the algorithms with a high number of citations in recent years.