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.

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.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic Genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the Genetic Algorithm and multi-objective optimization goals. The Genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

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 we investigate the performance of simulated annealing (SA) and Genetic Algorithm (GA) in preventive part replacement for minimum downtime maintenance planning. Therefore some evaluation criteria are explained in order to analyze the performance of the Algorithms. So it can be decided which Algorithm is more suitable to apply in preventive part replacement.

This paper presents a novel Meta-Heuristic method for solving an extended Markowitz Mean–Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the method of VaR calculation its minimizing methodology differs. if we use Historical Simulation which is applied in this paper then the curve would be nonconvex.On the other hand the Mean-VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new Meta- Heuristic approach based on combined Ant Colony Optimization (ACO) method and Genetic Algorithm (GA). The computational results show that the proposed Hybrid Algorithm has the ability to optimized Mean-VaR portfolio for small portfolio.

In scheduling, from both theoretical and practical points of view, a set of machines in parallel is a setting that is important. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view, the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program is necessary because the members of the program are performed in a parallel fashion, and this performance is executed according to some precedence relationship. This paper shows the problem of allocating a number of non-identical tasks in a multi-processor or multicomputer system. The model assumes that the system consists of a number of identical processors, and only one task may be executed on a processor at a time. Moreover, all schedules and tasks are non-preemptive.