Analysis of size-strength trade-off in green pelletization for manganese concentrate using multiobjective evolutionary algorithm (NSGA-II)

2016 ◽  
Author(s):  
Mohammad Nadeem ◽  
R. Venugopal
Keyword(s):  
Evolutionary Algorithm ◽  
Nsga Ii ◽  
Trade Off ◽  
Multiobjective Evolutionary Algorithm ◽  
Manganese Concentrate

scholarly journals HYBRID NSGA-II OPTIMIZATION FOR IMPROVING THE THREE-TERM BP NETWORK FOR MULTICLASS CLASSIFICATION PROBLEMS

2015 ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
Siti Mariyam Shamsuddin ◽  
Sultan Noman Qasem
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Classification Performance ◽  
Classification Problems ◽  
Nsga Ii ◽  
Bp Network ◽  
Multiobjective Evolutionary Algorithm

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.  

scholarly journals A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Zhiming Song ◽  
Maocai Wang ◽  
Guangming Dai ◽  
Massimiliano Vasile
Keyword(s):  
Regression Analysis ◽  
Probability Distribution ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Dimensional Manifold ◽  
Decision Space ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Piecewise Continuous

As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

Multi-Objective Optimization Design of Complex Mechanical and Electrical Products Based on Improved Evolutionary Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 1384-1388 ◽  
Author(s):  
Wei Wei ◽  
Li Hong Qiao
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Design ◽  
Design Space ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Trade Off ◽  
Pareto Optimal Set ◽  
Optimal Set

The design of complex mechanical and electrical products has to achieve various objectives and satisfy various constraints. In many cases, there are trade-off relationships between these objectives, and thus it is difficult to optimize these objectives simultaneously. This invokes the need of the multiobjective optimization to achieve these objectives collectively. In this paper, multiple objectives for complex mechanical and electrical products are optimized, simultaneously using an improved multiobjective evolutionary algorithm: ISPEA2. The results showed that ISPEA2 could generate uniformly a pareto optimal set in the design space and has better robustness and convergence than SPEA2 and NSGA-II.

scholarly journals Using Different Approaches to Approximate a Pareto Front for a Multiobjective Evolutionary Algorithm: Optimal Thinning Regimes forEucalyptus fastigata

2012 ◽  
Vol 2012 ◽  
pp. 1-27 ◽  
Author(s):  
Oliver Chikumbo
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Nsga Ii ◽  
Multiobjective Evolutionary Algorithm ◽  
Fitness Sharing ◽  
Pareto Ranking ◽  
Ranking Scheme ◽  
Volume Production ◽  
Nondominated Sorting

A stand-level, multiobjective evolutionary algorithm (MOEA) for determining a set of efficient thinning regimes satisfying two objectives, that is, value production for sawlog harvesting and volume production for a pulpwood market, was successfully demonstrated for aEucalyptus fastigatatrial in Kaingaroa Forest, New Zealand. The MOEA approximated the set of efficient thinning regimes (with a discontinuous Pareto front) by employing a ranking scheme developed by Fonseca and Fleming (1993), which was a Pareto-based ranking (a.k.a Multiobjective Genetic Algorithm—MOGA). In this paper we solve the same problem using an improved version of a fitness sharing Pareto ranking algorithm (a.k.a Nondominated Sorting Genetic Algorithm—NSGA II) originally developed by Srinivas and Deb (1994) and examine the results. Our findings indicate that NSGA II approximates the entire Pareto front whereas MOGA only determines a subdomain of the Pareto points.

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