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 study a new and robust procedure is presented to solve synthesis of isothermal reactor networks (RNs) which considers more than one objective function. This method uses NON-DOMINATED SORTING GENETIC ALGORITHM II ((NSGAII)) to produce structural modification coupled with quasi linear programming (LP) method for handling continuous variables. The quasi LP consists of an LP by adding a search loop to find the best reactor conversions as well as split and recycle ratios which are much easier to solve. To prevent complexity and ensure optimum solution, only ideal continuous stirred tank reactors (CSTRs), plug flow reactors (PFRs) and PFR with recycle stream are considered in producing reactor networks. Also, to avoid differential equations which appear in design equations of PFR reactors, CSTRs in series are replaced for each PFR. Results show that the proposed method finds better solutions than those reported in the literature.

Studying the bedrock geometry in mining and oil exploration operations to obtain its 2D pattern requires nonlinear reverse computations. Local optimization methods for solving nonlinear inverse problems are based on linearizing the changes of the model similar to a primary model and finding an objective function of minimum error from the parameters of the model; however, these optimization methods are not able to select a suitable primary function that is close enough to the general optimal value. That is to say, every objective function can have several minimum and maximum solutions. The lowest minimum is called the global minimum while the rest of them are named local minima. Therefore, in local inverse methods, the goal is to find the minimum of an objective function, and also an objective function might have a few local minima with different values. In this case, it is not suitable to use gradient-based methods for exploration purposes, unless the primary model is very close to the actual answer, which is outside the control of geological structures or the geometry of the subsurface. Despite the easy execution and high convergence rate of the local methods, there is the possibility of being trapped in local minima because these methods are dependent on the primary model, and also finding more than one optimized point in 2D or 3D simulations; this is why local optimization methods are considered deterministic ALGORITHMs. Multi-objective metaheuristic optimization ALGORITHMs are capable of searching the feasible region and they also provide a solution independent of the primary model. Searching the feasible region means finding all the feasible solutions for a problem. Each point in this region is representing a solution that can be ranked based on its value. One of the important differences between local optimization and metaheuristic methods is constraining. Constraining metaheuristic global optimization methods are only used for constraining the feasible region based on previous knowledge or estimation relations, which is different from constraining local optimization that is used for stabilizing inverse simulation. The ALGORITHM used in the present work includes NON-DOMINATED SORTING GENETIC ALGORITHM (NSGA-II). The NSGA-II is commonly used to solve problems with multiple, typically conflicting objective functions. This ALGORITHM is capable of being developed and also has a high potential for solving unbounded multi-objective problems. In the present study, NSGA-II ALGORITHM was verified and validated using the data produced by an imaginary and complex synthetic model. In the present research work, a hybrid technique of NSGA-II and TOPSIS ALGORITHMs was introduced and utilized as a viable search method for nonlinear modeling of the gravity data, and a substitute for the optimization methods. In order for a more precise examination of the performance of this ALGORITHM, the imaginary synthetic data were used both with no noise and with up to 10% Gaussian white noise (GWN). Based on the gravimetric data of the Moghan basin and Atacama Desert, Chile, the results obtained from ALGORITHM indicated good performance of the NSGA-II and (NSGAII)-TOPSIS ALGORITHMs.

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.

The main problem of water resources planning is the inappropriate allocation between different consumers. Water allocation planning is a complex, multi-variable, and multi-constraint problem, which requires advanced optimization methods to be solved. Classical optimization methods are facing some limitations such as being trapped in local optimum points, and difficulties in handling different variables. In this paper two of these methods including particle swarm optimization, PSO and multiobjective NON-DOMINATED SORTING GENETIC ALGORITHMs, NGGAII were explored and their efficiency in optimization water reservoir operation problems is compared. Dealing with the necessary of multiobjective programing accuracy, two single objective models was developed separately using PSO to verify the (NSGAII) results.

This study focuses on investigating how grid-stiffened composite shells behave under external hydrostatic pressure. The critical buckling load is calculated using the first-order shear deformation theory (FSDT) and the Ritz method. Various factors, including shell thickness, angle of helical stiffeners, rib section area, and the number of stiffeners, are examined to understand their impact on the buckling load. To optimize the design, three multi-objective optimization ALGORITHMs are employed: Nondominated SORTING GENETIC ALGORITHM II ((NSGAII)), Multiobjective Particle Swarm Optimization (MOPSO), and a hybrid method that combines (NSGAII) and MOPSO. The hybrid method intelligently divides the population into two groups and uses (NSGAII) and MOPSO to efficiently explore and exploit the solution space. The results yield a Pareto optimal front that showcases diverse solutions across different regions, providing decision-makers with the flexibility to select the solution that best fits their preferences. The solutions obtained through these ALGORITHMs are compared based on their diversity and distribution throughout the Pareto front.