In this paper, with emphasis on scheduling of head of crews for coach trains, a crew scheduling problem is presented in a network form with task arcs. To solve the problem, a meta heuristic ALGORITHM based on grouping EVOLUTIONARY ALGORITHM is developed. The grouping EVOLUTIONARY ALGORITHM contains two search methods which generate offspring from a parent chromosome; one of these methods is based on a logic constraints heuristic ALGORITHM, and the other one is relied on branch and bound approach. Computational results showed that combining grouping EVOLUTIONARY ALGORITHM and branch and bound approach generates good solutions, and for the problems that we knew their optimal solutions accomplish optimal outcomes. It is also shown that the ALGORITHM provides goods results for large scale real world problems.

Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic ALGORITHMs have been employed increasingly during recent years. In this study, a new ALGORITHM, namely Seeker EVOLUTIONARY ALGORITHM (SEA), is introduced for solving continuous mathematical problems, which is based on a group seeking logic. In this logic, the seeking region and the seekers located inside are divided into several sections and they seek in that special area. In order to assess the performance of this ALGORITHM, from the available samples in papers, the most visited ALGORITHMs have been employed. The obtained results show the advantage of the proposed SEA in comparison to these ALGORITHMs. At the end, a mathematical problem is designed, which is unlike the structure of meta-heuristic ALGORITHMs. All the prominent ALGORITHMs are applied to solve this problem, and none of them is able to solve.

In this paper, a new method is presented for solving the energy optimization problems. The goal is to find the optimal Energy Resources are Distributed (DER). so that minimizes the cost of the total operations. The methods we use in this paper is genetic ALGORITHMs, particle swarm ALGORITHM and modified cuckoo optimization ALGORITHM that genetic and particle swarm ALGORITHMs used to compare. Finally we use the proposed method which is modified cuckoo optimization ALGORITHM in the test Micro Grid (MG). Results show that the proposed ALGORITHM, in addition to providing electrical necessity of Micro Grid, it can be excellent of economically, which is the goal of this paper.

Many real-world applications require minimization of a cost function. This function is the criterion that figures out optimally. In the control engineering, this criterion is used in the design of optimal controllers. Cost function optimization has difficulties including calculating gradient function and lack of information about the system and the control loop. In this article, for the first time, gradient memetic EVOLUTIONARY programming is proposed for minimization of non-convex cost functions that have been defined in control engineering. Moreover, stability and convergence of the proposed ALGORITHM are proved. Besides, it is modified to be used in online optimization. To achieve this, the sign of the gradient function is utilized. For calculating the sign of the gradient, there is no need to know the cost-function’s shape. The gradient functions are estimated by the ALGORITHM. The proposed ALGORITHM is used to design a PI controller for nonlinear benchmark system CSTR (Continuous Stirred Tank Reactor) by online and offline approaches.

Topology optimization has been an interesting area of research in recent years. The main focus of this paper is to use an EVOLUTIONARY swarm intelligence ALGORITHM to perform Isogeometric Topology optimization of continuum structures. A two-dimensional plate is analyzed statically and the nodal displacements are calculated. The nodal displacements using Isogeometric analysis are found to be in good agreement with the nodal displacements acquired by standard finite element analysis. The sizing optimization of the beam is then performed. In order to determine the stress at each point in the beam a formulation is presented. The optimal cross-section dimensions by performing Isogeometric analysis are acquired and verified with the cross-section dimensions achieved by hiring bending stress and shear stress criteria, as well. The topology optimization of a two-dimensional simply supported plate continuum and a problem on three-dimensional continuum are optimized and presented. The results show that the minimum weight which is found by applying Isogeometric topology optimization gives better results compared to the traditional finite element analysis.

Different inventory control systems try to determine how much and when to order at the least relevant cost while maintaining a desirable service level for customers. In this article, a continuous review stochastic inventory system, with three objectives, is optimized. In this model, contrary to the traditional inventory models, customer service is not considered a shortage cost in the objective function. But the frequency of stock out occasions and the number of items stocked out annually are to be minimized. For determining the Pareto optimal set, multi-objective EVOLUTIONARY ALGORITHMs are used. First, NSGA-II, MOGA, VEGA, RWGA are developed. Then some improvements in NSGA-II mechanisms are made and R-NSGA-II is developed.Subsequently, these ALGORITHMs are examined for some criteria such as set coverage and spacing, and the best ALGORITHMs for each criteria are presented. The Result shows that R-NSGA-II has good scores for most criteria. Afterwards, Pareto optimal set is ranked using the method of global criteria.

In UAV navigation, the simultaneous minimization of distance traveled and the threat of radar detection is two criteria in many military applications. The use of many-objective EVOLUTIONARY ALGORITHMs in problems with conflicting goals can play an effective role in increasing the efficiency of these problems. In this research, the multi-purpose UAV route planning problem is considered and a many-objective optimization ALGORITHM is presented in a multi-purpose UAV route planning problem. The proposed solution method is a reference vector EVOLUTIONARY ALGORITHM (RVEA). In this research, a platform in the MATLAB software, which is called PlatEMO, has been used. The proposed method is compared and evaluated with other many-objective and multi-objective EVOLUTIONARY ALGORITHMs that have been presented recently and the results are obtained with a volume evaluation criterion that covers all the necessary categories (convergence, diversity and cardinality). Give, express. The problem has been tested on two groups of optimization problems. Comparing the presented results, it is observed that the reference vector EVOLUTIONARY ALGORITHM has a better performance than other compared ALGORITHMs, and the convergence rate, diversity and robustness of this ALGORITHM is higher than other compared ALGORITHMs.

Multi rotors UAVs have limited mobility due to the nature of the actuators. As a result, the multi-rotors UAV is unable to track the desired paths in space, and loss of mobility can be a limiting factor. Therefore, a comprehensive control is needed for this type of UAV. This research presents multi-objective optimization for control allocation problems based on EVOLUTIONARY game theory to solve the distribution of additional control input on the over-active system in real time on multi-rotor drones. For multi-objective optimization, an EVOLUTIONARY game theory-based approach with iterative dynamics is used to find the desired weight using the weighted sum method. The main idea of this method is that the best strategy or dominant solution can be chosen as the solution that remains among other non-dominant solutions. EVOLUTIONARY game theory considers strategies as an actor and examines how these strategies can survive using repetitive dynamics with the payment matrix. The numerical simulation results show the optimal weights selected by the EVOLUTIONARY game and how the efficiency changes in the dynamic repeater.