Recently, some researchers proposed a new algorithm for the bidirectional Evolutionary structural Optimization (BESO) in which adding and removing of elements is controlled by a signal parameter of removal ratio of volume (or weight). Nevertheless, their BESO improvement suffers from some restrictions like premature stopping of the iterative process due to the closeness of the adding and removing coefficients. In this study, the BESO method is modified such that eliminates the previous restrictions and increase its efficiency significantly. Several examples are presented for structural Optimization of a plate in two states of elastic and elastoplastic with stiffness and stress criteria. From the three examples, one can conclude that the modified BESO algorithm is more robust than the ESO and BESO algorithm previously proposed. It is also concluded the maximum stress and the performance index are improved in comparison with the case when the signal parameter is the same. Overall, the proposed modification prevent the premature termination of the Optimization process when the Optimization process is based on the hard kill method with both adding and removing the elements.

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.