Papers by Keyword: Multi-Objective

Abstract: The present work deals with multi-objective optimization of powder mixed electric discharge machining (PMEDM) when processing cylindrically shaped parts. In this work, the pulse on time Ton, the pulse off time T_off, the powder concentration C_p, the pulse current IP, and the server voltage SV were chosen for the optimization problem. Also, the surface roughness (SR), the material removal rate (MRR), and the electrode wear (EW) were chosen as three objectives for the investigation. Besides, the Taguchi method and the grey relational analysis (GRA) were applied for optimizing simultaneously three the SR, the MRR and the EW to find the optimum input factors. The impact of the process parameters on the overall goal was weighed. Additionally, optimum input factors of PMEDM process for multi-objectives were recommended.

Abstract: Economic dispatch (ED) is an important optimization task in power system which involves determination of the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying load constraints. There are three criteria in solving the economic load dispatch problem. They are minimizing the total generator operating cost, total emission cost and multi-objective (cost and emission). This paper proposes Moth Flam Optimization (MFO) algorithm for solving the economic dispatch problem with valve point effects and emissions. It determines the optimal generation schedule of generating units by minimizing three criteria. Two test systems consisting of 6 generating units and isolated microgrid have been used to illustrate the effectiveness of the proposed MFO method. The results obtained from MFO is compared with different algorithms. The results show better global convergence and also gives good optimum solution by reducing system generation cost and emission cost.

Abstract: Electro-Discharge Machining (EDM) is very popular for machining high-strength conductive materials for aerospace and automotive application. These machining involve a range of processing parameters. In order to optimize these for the best performance, a trade-off has to be decided for the responses achieved through machining. Conventional algorithms have long been replaced by advanced optimization algorithms. Performance of meta-heuristic algorithms in relation to traditional deterministic approaches for multi-modal, non-linear engineering problems is very promising in recent days. In this paper, a multi-objective optimization approach is applied using a population-based meta-heuristic algorithm called Passing Vehicle Search (PVS) for optimizing process parameters of various mathematical models formulated by different authors. Different approaches depending on case have been adopted for formulating the multi-objective PVS algorithm and pareto front is obtained for each case to extract the desired results. The performance of multi-objective PVS is compared with different intelligent computing models employed in prior studies and better results are shown in case of former. This approach can be extended to various mathematical models besides those covered in the paper to obtain better optimization results.

Abstract: Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

Abstract: Mill housing vibration influents the strip thickness fluctuation. The longitudinal deformation of rolling mill will directly affect plate thickness difference. The longitudinal deformation should be reduced , improving the mill stiffness. Hot rolling mill is seen as is as subject investigated . The improved GA is used to optimize the mill housing parameters. The optimal result is analyzed. The housing stiffness improves. Low order natural frequency improves after optimization, avoiding resonance and improving the housing dynamic stability.

Abstract: Inthis paper, the nonlinear optimal control problem is formulated as amulti-objective mathematical optimization problem. Harmony search (HS)algorithm is one of the new heuristic algorithms. The harmony search(HS) optimization algorithm is introduced forthe first time in solving the optimal power flow(OPF) solution. A case onoptimal power flow problem in the IEEE 30 bus system is presented to show themethodology’s feasibility and efficiency, compared with the existing optimalpower flow problem in power system methods, the search time of the HSoptimization algorithm is shorter and the result is close to the idealsolution, simultaneously.

Abstract: In supply chain management, supplier selection has a special importance. In this paper, a multi-objective model is developed based on decisions on quality, service level and cost of purchased goods. The difference of this research is to consider discount parameter into the model. A numerical example for three proposed suppliers is interpreted. The model has been solved in two steps: 1-single-objective, 2-multi-objective approach. According to Lpmetric solution, results can predict optimum selection of suppliers as well as the purchased goods amount. The results show that the single objective problem has better result than multi-objective function. This paper is organized by six sections: in next section, some previous studies and researches on supplier selection problem considering discount and non-considering discount have been discussed. In section 3, mathematical formulation of the supplier selection model considering all-unit and incremental with multiple-item is presented. In section 4, aLP metrics solution is dedicated. In section 5, a numerical example is given and the results are presented. Finally in section 6, conclusions of this research are discussed.

Abstract: Considering two goals of market share and location cost, this article builds a bi-objective location model. NSGA-II is utilized to acquire a Pareto non-dominated solution set. According to actual conditions such as cost constraints, decision-makers can choose solutions from non-dominated solution set. Furthermore, an approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) and minimum system’s cost under set covering are used to find out two reasonable solutions from the non-dominated solution set for decision-makers.

Abstract: The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

Abstract: Multi-objective optimization problems are very important, but it is hard to optimized or solved. Generalized ant colony optimization (GACO) algorithm is a new kind of ant colony optimization (ACO) algorithm developed in recent years. In this paper, we try to combine Multi-objective optimization problems with GACO algorithm, established a model for multi-objective GACO algorithm by absorbing state Markov chain, and present a method for estimating the convergence speed of multi-objective GACO algorithm. Simulation results show that the convergence speed of multi-objective GACO algorithm is faster than traditional multi-objective ACO algorithm.