objective optimization

Due to the increment of power demand, diminishing resources of fossil fuels and reduction of carbon dioxide out, the use of renewable energy resources is one of the important strategies in the developed countries. In this study, we use to meet electricity demand from renewable resources by as photovoltaic, batteries and wind turbines at times of peak production. The maximum amount of energy that solar panels and wind turbines can provide during any hour of the day is a function the amount of solar radiation and wind speed per hour. The aim is to minimize the cost of utilizing resources and maximize reliability. A non-linear programming model is proposed for the considered problem. Since the objectives are in conflict, for achieving a single optimal solution is not possible, therefore, NSGAII algorithm proposed for solving the problem

Over the last decades, optimization of structures has been the subject of extensive investigations and optimization using Genetic Algorithms (GA’s) has been studied intensively. Many researchers have been focused on the weight optimization of buildings mainly on steel structures rather than reinforced concrete (RC) frames. One of the challenges in multi-objective optimization of steel and RC buildings is to determine the balance between objective functions in order to find the Pareto-front. In this study, weighted sum method (WSM) optimization technique is utilized to solve RC frames optimization problems to minimize simultaneously cost and displacement, as objective functions, subject to the stress, displacement, size and other related constraints, based on the ACI 318-11 code. For this purpose, first, using WSM technique the two objective functions is converted in to single objective function and then GA applied for optimization procedure. Three design models of 4, 8 and 12-story RC frames are introduced as representative for short, mid and high rise buildings. Cross section of beams and columns are employed as design variables. The Pareto front for building models is obtained. To validate the results, they are compared to those obtained by ant colony optimization (ACO). The results illustrate the accuracy and rapidity of the proposed method.

Restructuring and deregulation have changed the power system planning and have introduced new objectives and challenges, especially in the field of Transmission Expansion Planning (TEP). On the other hand, the growing demand and shortage of energy and investment resources have caused special attention to the Demand Response (DR) programs. In this paper, dynamic multi-objective transmission expansion planning is carried out under reliability constraints in the market environment and the presence of demand bidding programs and price responsive bids. The novelties of this paper include the participation of demand bidding (DB) programs and price responsive bids in transmission expansion planning, new wholesale electricity market modeling considering the direct participation of DB programs and price responsive bids and finding the optimum amount of DB programs in each year in individual buses. These data can be sent as a signal to the DR suppliers from the market regulated entity to be considered in their long term planning. The proposed algorithm is implemented on the IEEE 24-bus network and its advantages, including reducing the investment cost and congestion and increasing the reliability, are shown.

Complexity of scientific issues and specialization of research fields, make team-working inevitable. Setting a good leader as team manager and selecting qualified staff as team members, are keys to the success of a team project. The problem of team formation with a leader in social networks is defined as finding the best team and the most appropriate leader, aiming to effectively collaborate towards a common goal and communication and personnel costs little. Despite the extensive research that has been done in the field of team formation in social networks, the challenge of finding a team of experts satisfying the capabilities required to do the project and leading to cost minimization, is unwavering. On the other hand, due to the increasing popularity and users of social networks, time-consuming algorithms are of other challenges in this field. Therefore having a project, the target is to find a team of experts, such that the team satisfies the required capabilities to complete the project, having a minimum communication and personnel cost. In this research, an algorithm based on the bi-objective optimization is proposed to identify such a team. To minimize both factors, a new compound cost function is defined based on linear combination of objectives. To evaluate the Bi-Objective TF, experiments are done using the DBLP real data. The results show better speed and efficiency comparing similar algorithms, due to removing superfluous nodes based on the experts’ capabilities and project features.

Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to the cloud. Task allocation is one of the most challenging issues in MCC considering neighboring mobile devices as the service providers. Given an application offloading request, the objective of the task allocation is to select service providers minimizing the completion time of the application offloading as well as consumed energy of all participating mobile devices while satisfying some QoS constraints. This paper models the task allocation problem in MCC as a constrained multi-objective optimization and proposes a two stages approach to solve the problem. In the first stage, a Multi-objective Resource Allocation based on Branch and Bound algorithm (MRABB) is designed to obtain Pareto solution set. In the subsequent stage, using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and given user’s preferences, the best compromise solution is determined. Furthermore, a context-aware software for Offloading in Mobile Cloud (OMC) is designed and implemented to collect contextual used in task allocation, and manage the offloading process. The results show the ability of the proposed multi-objective task allocation method to manage the trade-off between time and energy comparing to traditional algorithms.

Image segmentation is one of the most important and difficult steps in machine vision problems and achieving the desired results often requires satisfaction of different objectives. One approach to face this situation uses multi-objective fuzzy clustering of pixels in the feature space. This paper proposes a new strategy for search within the family of multiobjective differential evolution algorithms with the purpose of finding optimal partitions of pixels. Based on this¡ all of the encoded clusters in the population of one generation are clustered and each centroid of one donor vector is made with centers from a unique cluster. Through the search space dimension is reduced and searching for each centroid focused on the different area of input space while cluster centers of one fuzzy partition preserve separation. The performance of the proposed method is compared with two other multi-objective fuzzy clustering methods to segment a number of images from the Berkeley segmentation database. Visual and quantitative evaluations show that the proposed method has a better match with the ground truths than the other methods.

In this paper, simultaneous allocation of multi-distributed generation, capacitor bank and sectionaliser are utilized to improve efficiency of the distribution network. After placement of devices based on constant load model, technical performance of the system is evaluated under loads sensitive to voltage and frequency. Wind turbine and photovoltaic are renewable distributed generation units that along other devices are been studied. The proposed multi-objective functions are composed of reduction active and reactive power loss, improvement of voltage index and reliability indices. A hybrid optimization algorithm using Combination of firefly algorithm and particle swarm optimization is proposed to solve the multi-objective optimal allocation problem. Moreover, a method based on fuzzy mechanism is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed method is implemented on a IEEE 69-bus distribution network and also actual 100-bus distribution system in Khoy-Iran. The results approve the effectiveness of the proposed method with simultaneous placement of different devices for improving technical performance of distribution network.

Transient instability, voltage instability or a combination of both have been the cause of several power system blackouts all over the world in the recent years. Occurrence of a super-component contingency (SCC) that refers to multiple and simultaneous outages of grid facilities like a power plant or a multi-circuits line may lead to blackout if no remedial action schemes (RAS) are implemented. This paper proposes a new event-based RAS to overcome the transient and voltage instabilities caused by super-component contingencies through optimal generation shedding and load cut. To do this, a new multi objective framework is presented simultaneously optimizing the competing objective functions of transient and voltage stability margins and generation shed and load cut amount. Multi objective decision making is performed using a combination of analytical hierarchy process (AHP), modified augmented  constraint method and technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of both the proposed model and MODM solution approach is extensively illustrated on a simulated model of Iran’s power system in 2014.

In this research, an ice thermal energy storage (ITES) and a phase change material (PCM) system are coupled to an air-conditioning system as a full cooling load storage system. The system is modeled and examined from exergy, economic and environmental point of views. a multi-objective optimization algorithms, based on genetic algorithm, is applied to achieve the optimal decision parameters which go to the optimal objective functions including exergy efficiency and total cost rate. The optimum point from pareto frontier is found by using TOPSIS decision making method. Finally, the system is compared with the conventional system from electricity consumption, CO2 emission value and payback period aspects. The results indicate that the electricity consumption decrements relative to the conventional system are 4.59% and 7.58% for ITES and PCM system respectively as well as the annual CO2 production of PCM and ITES system is 27.2% and 17.8% lower than the conventional system respectively.

Application of distributed generation particularly wind power plants in power systems, is rapidly increasing due to their various advantages. Considering the effects of these new resource impacts in power system planning, particularly TEP problem is inevitable, due to their essential role in changing the optimal plans. In this paper, a multi-objective optimization method is presented using DC load-flow in order to present a strong model for solving TEP problem. Considering uncertainties of wind farms generation and network load in the form of probabilistic variables, as well as the investment and maintenance costs in the objective function are the main advantages of the proposed algorithm. Multi-objective optimization problem has been analyzed using Imperialist Competitive Algorithm (ICA) which is one of the newest evolutionary optimization algorithms applied to power systems. To study the effects of wind farms on TEP problem and validate the proposed method, necessary changes have been made in RTS-IEEE 24-bus network and the presented algorithm has been applied to them. The results have been compared to optimum plans which were searched by GA Algorithm

Objective of this research is to Efficieny Enhancement and Polutant Gas Emission Reduction in a Benchmark Turbo-Compressor Gas Engine. Using Heat Recirculation of Flue Gas are studied and using a shell&tube and plate finn heat exchanger for simple gas cycle is considered and optimized. Thermodynamic modeling of the cycle is performed using energy and exergy analysis. Thermo-hydraulic modeling of the recuperators is performed using the modified heat transfer analysis and ε-NTU method in order to estimate the heat exchangers area, effectiveness and pressure drop. Optimization is considered as the multi-objective evolutionary algorithm in which one objective is maximizing the cycle exergetic efficiency, the second is minimizing the capital payback time of the recuperator and the other one is minimizing the Polutant Gas Emission for both of heat exchangers. A set of optimal solutions namely as the Pareto frontier are obtained and a final optimal solution are selected using the Bellman-Zadeh fuzzy decision-making approach, TOPSIS and LINMAP methods and the best solutions are reported. In final by comparing the heat exchangers, The best of heat exchanger was selected. Finally, by comparing optimal shell&tube and plate finn heat exchanger, The best heat exchanger has been chosen for Turbo-Compressor. The final improvement Heat Recirculation, Resulted in a 19.1 percent increase in exergy efficiency and reduce NOx emissions rate is 17.93 percent.