Differential Evolution algorithm (DE), one of the evolutionary algorithms, is a new optimization technique capable of handling non-differentiable, non-linear and multimodal objective functions. DE needs a large run time for optimizing the complex objective function. Thus, an attempt to speed up DE is necessary. This paper introduces a modification on original DE that enhances the convergence rate by reducing vector dispersal at any iteration. Our Adaptive Differential Evolution algorithm (ADE) utilizes variable scaling parameter (F) against constant scaling parameter in original DE at any iteration. The proposed ADE is applied to optimize three non-linear chemical engineering problems. The obtained results have been compared with those results obtained using DE. The considered comparison criteria are the vectors dispersal, convergence history (run time and number of iterations that led to reach to global optimum) and error in any iteration. As compared to DE, ADE is found to perform better in locating the global optimal solution, reduces the memory and computational efforts by reducing the number of iterations required to reach the global optimal solution for all the considered 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.

One of the port planning problems that has been noticed in many papers and research is the berth planning problems. Berth planning includes two sub-problems; Berth Allocation Problem (BAP) and Quay Crane Assignment Problem (QCAP). This paper develops one mathematical model by integrating these two sub-problems. The berth allocation and quay crane assignment model (BAQCAP) is solved by two metaheuristic algorithms; Taboo Search (TS) and Ant Colony Optimization (ACO). On the other hand, the berth plan is located in a disturbed environment; unexpected events may occur during the execution of the plan, making it infeasible or challenging to do the initial berth plan. These unexpected events are known as disruptions, which can impose additional costs on the port or make the initial berth plan infeasible. For this reason, The primary purpose of this paper is on the berth plan recovery in the disrupted situation. the Berth plan is recovered with two methods; Global recovery and local recovery. This paper compares global and local recovery to identify the optimal method for berth plan recovery. The numerical results show the optimal performance in the local recovery method. In this paper, the data from Shahid Rajaei port is used.

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.

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.