In the current paper, we have assimilated fuzzy techniques and optimization techniques, namely DIFFERENTIAL evolution, to put forward a modern archive-based fuzzy EVOLUTIONARY ALGORITHM for multi-objective optimization using clustering. The current work account for the application of a cluster associated approach. Specific quantitative cluster validity measures, i. e., J-measure and Xie-Beni, have been referenced to carry out the appropriate partitioning. The proposed ALGORITHM introduces a new form of strategy which attempts to benefit the feasible search domain of the ALGORITHM by minimizing the analysis and exploration of less beneficial search scope. This clustering method yields a group of trade-off solutions on the ultimate optimal pare to front. Eventually, these solutions are united and maintained in an archive for further evaluation. The current work summarizes and organizes an archive concerned with excellent and diversified solutions in an effort to outline comprehensive non-dominated solutions. The degree of efficiency is revealed with respect to partitioning on gene expression and real-life datasets. The proposed ALGORITHM seeks to reduce the function assessment analysis and maintains a very small working population size. The effectiveness of the proposed method is presented in comparison with some state-of-art methods.

In this context, a novel structure has been proposed for simple DIFFERENTIAL EVOLUTIONARY (DE) ALGORITHM to solve optimal recloser placement. For this, an operator is added to DE ALGORITHM to adapt concept of the problem. Other contribution of this work is formulating a novel objective function. The proposed objective function has been formulated to improve four reliability indices which consists of four terms; i.e. System Average Interruption Duration Index (SAIFI), Cost of Energy Not Supplied (CENS), Average Interruption Frequency Index (MAIFI) and System Average Interruption Frequency Index (SAIFI). Simulation has been performed in 37 bus test system and the results of the proposed technique compared the related results of PSO ALGORITHM.

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.

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.

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.

Reconfiguration of the distribution network as well as the optimal use of distributed generation resources in the distribution system are very effective methods to reduce losses and improve the voltage profile or in other words power quality in the electricity distribution system. In recent years, researchers have paid attention to the use of distributed production resources. The use of these resources has several advantages, the most important of which are the reduction of network losses and the increase of voltage stability. In this study, a DIFFERENTIAL EVOLUTIONARY ALGORITHM is presented to solve the desired optimization problem to reduce losses and bus voltage deviation. On the other hand, since the system load is always changing and is not constant, therefore, for the simulation results to be close to the real conditions of the distribution network, it is suggested in this study that the uncertainty of the consumption load should also be modeled and applied to the optimization problem. The mentioned problem has different discrete and continuous variables, which necessitates the use of ALGORITHMs that can search in discrete and continuous spaces. Therefore, to overcome this issue and apply different constraints to the problem, the DIFFERENTIAL EVOLUTIONARY ALGORITHM has been used. The mentioned method has been tested on a standard 33-bus test network and the results have been compared in three different scenarios. In the second and third scenarios, reconfiguration of the distribution network has been resolved in the absence/presence of scattered production units (wind turbines) respectively. The results of the proposed method have been compared with other references. The results show that the proposed DIFFERENTIAL EVOLUTIONARY ALGORITHM performs better than the other two ALGORITHMs and has achieved better results.