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.

In this paper, a NON-DOMINATED SORTING GENETIC ALGORITHM-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. In order to illustrate the performance of the proposed method, 33-bus and 69-bus distribution networks have been employed which have led to the desired results.

In a multi-modal multi-objective route planning problem, the main purpose is finding an optimal route between the origin and destination, which is a combination of multi-transportation modes, pairs by considering multifitness function. Most of multi-objective problems are solved by assigning a weight to each objective function and using a linear averaging of the objectives as a distinct objective function. These methods have some weaknesses such as inability in searching the problem space and a need to normalize the objective functions. Therefore, in this paper, a NON-DOMINATED SORTING GENETIC ALGORITHM (NSGA-II) has been used to solve the multi-modal multi-objective routing problem. This ALGORITHM proposes a set of NON-DOMINATED routes that has no absolute superiority to each other. Finally, the optimal route was determined using TOPSIS method from this set. The intended objective functions in this research are the lowest number of changes in transportation means, fare and time during the path. Moreover, five transportation modes including subway, taxi, bus, BRT, and walking transportation modes have been considered as means of transportation inside the mentioned network. This ALGORITHM was implemented in a part of Tehran transportation network and results showed that the proposed NSGA-II ALGORITHM proposed a better route in 89% and 87% of the routing cases than those of the GENETIC and the simulated annealing ALGORITHMs respectively.

Today, marketing models and issues are increasingly becoming complex, leading to the use of complicated solutions. The application of novel methods in marketing and advertising planning is of interest to researchers of these fields. This has led to an increase in utilization of metaheuristics based on evolutionary computations and artificial intelligence.Regarding web advertising characteristics and current pricing strategies, in this article a hybrid pricing strategy was created based on variables of Costper-thousand-impressions (CPM) and Cost-per-click (CPC). Consequently, the new multi-objective optimization decision model was proposed based on this strategy. This model considered the interests of both websites managers and web advertisers. Since this new model is a high dimensional multiobjective optimization model, NON-DOMINATED SORTING GENETIC ALGORITHM II (NSGA-II) was used to solve it. At last, a computational example was used and numerical results obtained from the simulation proved the effectiveness of the model and ALGORITHM.