Urban land use planning which is one of the main components of urban planning typically defined as a multi-objective planning problem in optimal use of urban space and existing facilities. Among numerous land use maps, urban planners are usually interested in choosing the map which is contiguous to the optimal land use map of an interested vision. Reference point multi-objective optimization algorithms provide possibility of introducing the optimal values for different objectives as a reference point and producing optimal solutions near to reference points. In this study, the implementation and efficiency of Reference-Point-Nondominated Sorting Genetic algorithm II (R-NSGA II) for urban landuse allocation is investigated and a method for chromosomes coding is proposed. Maximizing compatibility of adjacent land use, land suitability, accessibility to roads and main socio-economic centers, and minimizing resistance of land use to change are defined as the main objectives. Then the optimal values of objectives were introduced to the algorithm as reference points. Consequently, planners will be able to select within proposed land use maps according to their priorities. The results of land use allocation modeling for Shiraz city in 2011 indicate that the decision maker is able to choose a better decision with more reliability comparing to situations with a single solution. This achievement indicates proposed model ability for simulation of different scenarios in land use planning.

The use of distributed generation units in distribution networks has attracted the attention of network managers due to their great benefits. In this research, the location and determination of the capacity of distributed generation (DG) units for different purposes has been studied simultaneously. The multi-objective functions in optimization model are reducing the losses of the system line, reducing voltage deviation, increasing voltage stability margin, and decreasing network's short circuit when DG units are considered in the distribution network (DN). To calculate the values of mentioned multi-objective functions, a backward and forward sweep load-flow and a short circuit calculation are used. To solve the problem, a multi-objective optimization algorithm called improved Non-dominated Sorting genetic algorithm–, II (INSGA-II) is used. This algorithm leads to the creation of various responses that the user can choose, as needed, for each one. A tradeoff method, based on fuzzy set theory, is used to obtain the best optimal solution. The proposed method is examined on the IEEE 33-bus test case while considering different scenarios. In the end, the feasibility and the effectiveness of the proposed algorithm for optimal placement and the sizing of DG in distribution systems have been proved.

Objective The present study aimed to provide a decision model in Internet advertising planning using multiobjective genetic algorithm. The proposed model is a model for distributing advertising resources through the web to optimize the effect of advertising, based on research literature and according to the characteristics of advertising through the web. This model can simultaneously consider the interests of network managers and advertisers. Methodology The present study is in the category of descriptive research in terms of method and nature and is a survey in terms of implementation and also applied in terms of purpose. In this research, since the proposed model is a multi-objective optimization model with high dimensions, the multi-objective genetic optimization algorithm has been used to solve it. Findings In this study, unlike previous studies, by simultaneously considering the conflicting goals of applicants for advertising through the web (reducing advertising costs) and webmasters (increasing profits from the provision of services), about How to better optimize the allocation of advertising resources to the website was discussed and a new decision model was presented that had two conflicting goals. In fact, this multi-objective model not only maximizes website revenue but also reduces the cost to the applicant of advertising; therefore, the mentioned model can be the basis of the work of these two. On the other hand, based on the characteristics of advertising through the web and existing pricing strategies, a hybrid pricing strategy was created based on the variables "cost per thousand views" and "cost per click in this research". Then, a new multi-objective optimization decision model based on this strategy was proposed. In this model, the interests of webmasters and advertisers are considered. Finally, by providing a computational example and numerical results of the simulation, the effectiveness of the model and algorithm is proved. Conclusion The simulation results showed that the optimization model and algorithm are justified and feasible. Also, the set of optimal Pareto answers obtained from solving the model can satisfy the webmasters and applicants for advertising. Using this model, they interact and compromise and try to consider the interests of another person. Considering that by solving the proposed model, unlike other models, the interests of both stakeholders have been considered, the answer set is included in the win-win strategy. Therefore, since the validation of this model is done through simulation, in practice, network administrators can when coding ads on web pages by applying the mathematical relationships provided in the proposed model, the method of calculating the cost of applicants for advertising is logical. And provide a list of possible suggestions to the applicant. In this list, different combinations of simultaneous decision variables at the desired level, by maximizing the income of network managers, minimize the costs of each applicant according to their opinion, which leads to the adoption of more efficient pricing strategies.

Structural damage not only changes the dynamic characteristics of the structure, but also it may lead to complete destruction of the structure in some cases. Since early identification of damage can prevent such catastrophic events, structural health monitoring and damage detection has absorbed the attention of the civil, mechanical and aerospace engineers in the last decades. An effective health monitoring methodology not only can provide information about the global serviceability of the monitored structure, but also it can help the engineers to prepare cost-effective rehabilitation programs based on the obtained details about the health of the structure and its members. Different methods have been proposed for structural damage identification and estimation. Vibration-based methods consider the changes in the structural modal parameters, like natural frequencies and associated mode shapes, and/or their derivatives, like modal flexibility and residual force vector, for damage identification and quantification. Considering their acceptable sensitivity to widerange of structural damages, vibration-based methods are considered as one of the most practical approaches for structural fault prognosis. Employing vibration parameters to define the damage detection problem as a model updating problem, is one of the well-known strategies that can return both the damage location and extent in different types of engineering structures. Such methods can be solved with optimization algorithms to find and report the structural damage in terms of the global extremums of a damage-sensitive objective function. In this paper a new model updating approach for health monitoring and damage localization and quantification in engineering structures is presented. At first, a damage-sensitive objective function, which is based on the error function between the modal data of the monitored structure and its analytical model, is proposed. This objective function is formulated by means of the point-by-point matching strategy to minimize the difference between two models. Modal natural frequencies and the associated mode shape vectors are directly fed to the objective function and this can result in an easy assessment methodology to check the convergence rate of the function. Moreover, in such a case, the objective function uses the sensitivity of both these parameters for damage identification. The proposed inverse problem is solved using Moth-Flame Optimization (MFO) algorithm which has been inspired form spiral convergence of Moths toward artificial lights. From mathematical point of view, updating the position of the Moths with respect to the Flames – which are the best solutions obtained during iterations– , reduces the probability of being trapped in the local extremum points and also, ensures the convergence of the algorithm to its global optimal solution. The applicability of the method was evaluated by studying different damage patterns on three numerical examples of engineering structures: a seven-story shear frame, a simple beam with 10 elements, and a planar truss with 29 elements. In all these studies, damages were simulated as reduction in the stiffness matrix of the damaged elements. Different issues, like noise effects, were considered and their impacts on the performance of the proposed method were investigated. Furthermore, comparative studies were carried out to discuss the advantages and drawbacks of the introduced method as well as the employed techniques. The obtained results indicate that the method is an effective strategy for vibration-based damage detection and localization in engineering structures.

Using linear, nonlinear, and dynamic planning methods for water resources management has been common since a long time ago, but owing to some deficiencies, today much attention is paid to heuristics methods. Among the optimization algorithms, the Mothfire algorithm can be considered. In this paper, the optimization of the flood management plan was carried out using the Moth-fire algorithm. In order to consider the flood damage in each month, the estimated damage values are determined according to the floods routing with different return periods in the downstream of the dam using MATLAB software. The sum of the expected damage of flood and lack of need supply in the objective function will be minimized using the Moth-fire algorithm. The results of a case study carried out on the Aras dam indicate the efficiency of the proposed optimization model in supplying the needs and reducing the flood damage in the downstream.

The focus is on the longevity and energy efficiency of wireless sensor networks (WSN). WSNs face many obstacles in terms of data transmission. WSNs face difficulties in reducing energy output and shortening life cycles, including node configuration, leader selection, and optimal routing selection. The provisioning of nodes, selection of cluster leaders, and optimal paths have all been recommended using many current methods. However, none of the currently used methods yield sufficient grid energy optimization results. Therefore, this study proposes a modified Moth Flame Optimization algorithm (MFOOA). Nature passed it on to us. The main inspiration for this optimizer is the lateral flight pattern used by Moths in nature. At night, the Moth maintains a constant angle to the moon. This is a particularly efficient way to drive long distances in a straight line. Nevertheless, artificial light is everywhere around these amazing creatures, encircling them in a fruitless and deadly spiral. Here, this behavior is theoretically modeled for optimization. The suggested program places the sensor nodes using the Flame optimization technique. These sensor nodes might be either dynamic or static depending on the network scenario. The cluster head and the optimum route are chosen using this technique. Within the predetermined search space, it also does phase balancing between the exploration and development phases. In terms of residual energy, sensor node lifetime, used energy, end-to-end latency, and a maximum number of cycles, it differs from current classical and swarm intelligence (SI) techniques. According to the results, MFOOA is superior to its counterpart.

Uncertainty analysis in systems evaluation and management has been considered as a logical aspect in engineering estimates in recent decades. Uncertainty generally implies that there is no complete knowledge of the behavior of a system and the specific values of its variables. Currently, the uncertainty problem has been one of the topics of interest in research, decision-making and design in the field of water science and engineering. Furthermore, field observational evaluations have shown that hydrological phenomena are inherently random and can follow probabilistic functions. In the past, univariate probability models have been used to predict hydrological events. But today, it has been shown that hydrological phenomena such as precipitation, runoff, flood, and drought are random and multivariate and should be expressed by the characteristics of intensity, duration and amount. The efficiency of copula functions has been proven as an effective technique in multivariate analysis of hydrological events. This study was aimed to develop the probabilistic-fuzzy framework according to the existing gap in the previous studies. Therefore, long-term rainfall and runoff information were analyzed for generating the decision-making system in Barz Plain, Khuzestan province, Iran. Four parameters of maximum rainfall rate and depth, peak flow and volume of runoff have been analyzed to generate the time series of information. The developed conceptual structure could be addressed in four steps. In the first step, an attempt was made to analyze the relationship between the flood hydrographs in the period from 1973 to 2018 in Barz Plain. Barz plain is located in the eastern north of Khuzestan province, Iran within 31° 18' to 31° 28' latitude and 50° 18' to 51° 26' longitude. Next, a probabilistic model was developed by the MATLAB program. In this model, marginal distribution functions, bivariate frequency analysis and calculation of return periods for rainfall characteristics were evaluated. Moreover, a fuzzy set analysis sub-program was prepared to transfer the effects of rainfall to the runoff. This process was developed within the framework of a multi-objective optimization program that was solved using the NSGAII method. The hydrological information used in this study includes the amount of rainfall and runoff recorded in daily time steps. Analysis of available data revealed about 37 rainfall events that led to the flood. Rainfall characteristics (maximum daily rate (mm/day) and cumulative rainfall depth in one event (mm)) and two characteristics of runoff hydrograph (hydrograph peak flow (m3/s) and runoff volume (million cubic meters (MCM)) were calculated from daily rainfall and runoff information. Therefore, a probabilistic decision model based on copula multivariate functions was developed to predict the variables at different return periods. The relationship between rainfall rate and depth with peak hydrograph flow and runoff volume for flood events over a 37-year period was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using a multi-objective optimization genetic algorithm based on the Non-dominated Sorting to find the extreme points. The obtained solutions were used as a fuzzy response to calculate the runoff of the Barz plain in Khuzestan province in southwestern Iran. The relationship between rainfall and runoff characteristics showed that the maximum rainfall rate and the peak runoff discharge can be inspired by fuzzy theory and create a logical relationship. The results obtained by Chi-Squared, Anderson Darling and Kolmogorov Smirnov tests for the marginal frequency functions indicated that the Generalized Gamma, Log Normal, Generalized Extreme Values, and Log Pearson functions were the best options for estimating the maximum rate and depth of rainfall and peak flow and volume of runoff, respectively. Correlation coefficient was calculated to evaluate the bivariate model performance for the variables. The results of the maximum likelihood estimator to determine the superior joint function showed that Archimedean Clayton copula fited better than others for rainfall characteristics. Based on the developed concepts, the predicted runoff for Barz plain was estimated between 650 to 850 MCM for a 100-year return period. This return period is recommended for reservoir dam design. Consequently, in the 25-year return period, which is an appropriate time scale for flood diversion systems of water storage reservoirs, flood volume of 140 to 173 MCM with a maximum flow of 850 m3/s has been obtained. This flood can be caused by a rainfall of 78 mm/day or a depth of 137 mm

To derive optimal operation policies from multi-reservoir systems, applying the conflict multiple goals simultaneously is very important. In order to, this paper presents a multi-objective optimization-simulation model, which is consist of agriculture and minimum flow goals in a three dam water resources system. For this purpose, a discrete hedging rule with a Non-dominated Sorting genetic algorithm has been coupled to minimize the modified shortage index over a series of hydrological record of 48 years. The evaluation metrics are diversity metric and standard deviation, which obtained values of each are equal to 0.357 and 0.0111 respectively. The results show the efficient performance of this algorithm to obtain Pareto frontier. Also, multi-objective algorithms present a set of optimum solutions for users instead of a solution, thus it helps to make decision in supplying the conflict goals of agriculture and minimum flow in different and complicate operation conditions such as drought periods easily.