optimization algorithm

In this study, an optimization algorithm based on the generalized Laguerre polynomials (GLPs) as the basis functions and the Lagrange multipliers is presented to obtain approximate solution of nonlinear fractional optimal control problems. The Caputo fractional derivatives of GLPs is constructed. The operational matrices of the Caputo and ordinary derivatives are introduced. The established scheme transforms obtaining the solution of such problems into finding the solution of algebraic systems of equations by approximating the state and control variables using the mentioned basis functions. The method is very accurate and is computationally very attractive. Examples are included to provide the capacity of the proposal method.

Feature selection is the process of identifying relevant features and removing irrelevant and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal loss of efficiency. One of the feature selection approaches is using optimization algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2021 that were designed and implemented on a wide range of different data. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used.

Recently, many optimization algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search unknown and multidimensional spaces and find the optimal solution the shortest possible time. In this paper we present a new modified differential evolution algorithm. Optimization algorithms typically have two stages of exploration and exploitation. Exploration refers to global search and exploitation refers to local search. We used the same differential evolution (DE) algorithm. This algorithm uses a random selection of several other search agents to update the new search agent position. This makes the search agents continually have random moves in the search space, which refers to the exploration phase but there is no mechanism specifically considered for the exploitation phase in the DE algorithm. In this paper, we have added a new formula for the exploitation phase to this algorithm and named it the Balanced Differential Evolution (BDE) algorithm. We tested the performance of the proposed algorithm on standard test functions, CEC2005 Complex and Combined Test Functions. We also apply the proposed algorithm to solve some real problems to demonstrate its ability to solve constraint problems. The results showed that the proposed algorithm has a better performance and competitive performance than the new and novel optimization algorithms.

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorithm has been called Imperialist Competitive Algorithm (ICA). The ICA is a population-based algorithm where the populations are represented by countries that are classified as colonies or imperialists. This paper is going to present a modified ICA with considerable accuracy, referred to here as ICA2. The ICA2 is tested with six well-known benchmark functions. Results show high accuracy and avoidance of local optimum traps to reach the minimum global optimal.Three important policies are in the ICA, and assimilation policy is the most important of them. This research focuses on an assimilation policy in the ICA to propose a meta-heuristic optimization algorithm for optimizing function with high accuracy and avoiding to trap in local optima rather than using original ICA by a new assimilation strategy.

WirelessSensor Network (WSN) is one of the most important technologies of the XXI century whichis becoming the next step in information revolution. The WSNis divided in two main categories, Homogenous Wireless Sensor Networks and Heterogeneous Wireless Sensor Networks. Heterogeneous wireless sensor network consists of several nodes with different functions and characters. Minimizing the number of super nodes in these networks is one of the challenging issues. Several approaches have been presented to solve this problem up to now; such as Genetic Algorithm, Bee Algorithm, PSO Algorithm and etc. In this paper a novel meta-heuristic algorithm Cuckoo Search Algorithm (CSA), is introduced to solve this problem. The main contribution of this paper is to reach an optimum trade-off between the number of super nodes and network efficiency. MATLAB, simulation toolkit, is used to simulate the efficiency of this method. Simulation results show that proposed algorithm quickly finds a good solution and has better performance than Genetic based algorithm.