scholarly journals An Improved Multiobjective Algorithm: DNSGA2-PSA

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Dan Qu ◽  
Xianfeng Ding ◽  
Hongmei Wang
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
High Dimensional ◽  
Nsga Ii ◽  
Equal Importance ◽  
Multiobjective Optimization Problems ◽  
Diversity Preservation ◽  
Ranking Scheme ◽  
Nondominated Sorting ◽  
Better Than

In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

Comprehensive Survey of the Hybrid Evolutionary Algorithms

2013 ◽  
Vol 4 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Wali Khan Mashwani
Keyword(s):  
Evolutionary Algorithms ◽  
Pareto Front ◽  
Optimization Problems ◽  
Mathematical Formulation ◽  
Multiobjective Evolutionary Algorithms ◽  
Nsga Ii ◽  
Pareto Optimal Set ◽  
Multiobjective Optimization Problems ◽  
Comprehensive Survey ◽  
Optimal Set

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.

scholarly journals Performances Assessment of MOMA-Plus Method on Multiobjective Optimization Problems

2020 ◽  
Vol 13 (1) ◽  
pp. 48-68
Author(s):  
Alexandre Som ◽  
Kounhinir Some ◽  
Abdoulaye Compaore ◽  
Blaise Some
Keyword(s):  
Multiobjective Optimization ◽  
Comparative Study ◽  
Pareto Front ◽  
Optimization Problems ◽  
Test Problems ◽  
Nsga Ii ◽  
Multiobjective Problems ◽  
Multiobjective Optimization Problems ◽  
Non Linear

This work is devoted to evaluate the performances of the MOMA-plus method in solving multiobjective optimization problems. This assessment is doing on the complexity of its algorithm, the convergence and the diversity of solutions in relation to the Pareto front. All these parameters were evaluated on non-linear multiobjective test problems and obtained solutions are compared with those provided by the NSGA-II method. This comparative study made it possible tohighlight the performances of MOMA-plus method for solving non-linear multiobjective problems.

scholarly journals Performances Assessment of MOMA-Plus Method on Multiobjective Optimization Problems

2020 ◽  
Vol 13 (1) ◽  
pp. 48-68
Author(s):  
Alexandre Som ◽  
Kounhinir Some ◽  
Abdoulaye Compaore ◽  
Blaise Some
Keyword(s):  
Multiobjective Optimization ◽  
Comparative Study ◽  
Pareto Front ◽  
Optimization Problems ◽  
Test Problems ◽  
Nsga Ii ◽  
Multiobjective Problems ◽  
Multiobjective Optimization Problems ◽  
Non Linear

This work is devoted to evaluate the performances of the MOMA-plus method in solving multiobjective optimization problems. This assessment is doing on the complexity of its algorithm, the convergence and the diversity of solutions in relation to the Pareto front. All these parameters were evaluated on non-linear multiobjective test problems and obtained solutions are compared with those provided by the NSGA-II method. This comparative study made it possible tohighlight the performances of MOMA-plus method for solving non-linear multiobjective problems.

An Improved Nondominated Sorting Algorithm

2012 ◽  
Vol 3 (4) ◽  
pp. 20-42
Author(s):  
André R. da Cruz
Keyword(s):  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Computational Cost ◽  
Sorting Algorithm ◽  
Nsga Ii ◽  
Worst Case ◽  
New Strategy ◽  
Nondominated Sorting ◽  
Correct Level ◽  
Better Than

This paper presents a new procedure for the nondominated sorting with constraint handling to be used in a multiobjective evolutionary algorithm. The strategy uses a sorting algorithm and binary search to classify the solutions in the correct level of the Pareto front. In a problem with objective functions, using solutions in the population, the original nondominated sorting algorithm, used by NSGA-II, has always a computational cost of in a naïve implementation. The complexity of the new algorithm can vary from in the best case and in the worst case. A experiment was executed in order to compare the new algorithm with the original and another improved version of the Deb’s algorithm. Results reveal that the new strategy is much better than other versions when there are many levels in Pareto front. It is also concluded that is interesting to alternate the new algorithm and the improved Deb’s version during the evolution of the evolutionary algorithm.

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.

Improved Trust Region Based MPS Method for High-Dimensional Expensive Black-Box Problems

2013 ◽  
Author(s):  
George H. Cheng ◽  
Adel Younis ◽  
Kambiz Haji Hajikolaei ◽  
G. Gary Wang
Keyword(s):  
Optimization Problems ◽  
Trust Region ◽  
Black Box ◽  
High Dimensional ◽  
Test Problems ◽  
Design Variables ◽  
Low Dimensionality ◽  
Improve Algorithm ◽  
Improved Performance ◽  
Better Than

Mode Pursuing Sampling (MPS) was developed as a global optimization algorithm for optimization problems involving expensive black box functions. MPS has been found to be effective and efficient for problems of low dimensionality, i.e., the number of design variables is less than ten. A previous conference publication integrated the concept of trust regions into the MPS framework to create a new algorithm, TRMPS, which dramatically improved performance and efficiency for high dimensional problems. However, although TRMPS performed better than MPS, it was unproven against other established algorithms such as GA. This paper introduces an improved algorithm, TRMPS2, which incorporates guided sampling and low function value criterion to further improve algorithm performance for high dimensional problems. TRMPS2 is benchmarked against MPS and GA using a suite of test problems. The results show that TRMPS2 performs better than MPS and GA on average for high dimensional, expensive, and black box (HEB) problems.

scholarly journals Fast Nondominated Sorting Genetic Algorithm II with Lévy Distribution for Network Topology Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Maoqing Zhang ◽  
Lei Wang ◽  
Zhihua Cui ◽  
Jiangshan Liu ◽  
Dong Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Network Topology ◽  
Optimization Problems ◽  
Selection Strategy ◽  
Nsga Ii ◽  
Comparison Results ◽  
Practical Engineering ◽  
Network Topology Optimization ◽  
Nondominated Sorting

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

Optimization of Vehicle-Borne Radar Antenna Pedestal Based on Modified NSGA-II

2014 ◽  
Vol 945-949 ◽  
pp. 2241-2247
Author(s):  
De Gao Zhao ◽  
Qiang Li
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Population Diversity ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sorting Process ◽  
Definition Of ◽  
Radar Antenna ◽  
Modified Algorithm

This paper deals with application of Non-dominated Sorting Genetic Algorithm with elitism (NSGA-II) to solve multi-objective optimization problems of designing a vehicle-borne radar antenna pedestal. Five technical improvements are proposed due to the disadvantages of NSGA-II. They are as follow: (1) presenting a new method to calculate the fitness of individuals in population; (2) renewing the definition of crowding distance; (3) introducing a threshold for choosing elitist; (4) reducing some redundant sorting process; (5) developing a self-adaptive arithmetic cross and mutation probability. The modified algorithm can lead to better population diversity than the original NSGA-II. Simulation results prove rationality and validity of the modified NSGA-II. A uniformly distributed Pareto front can be obtained by using the modified NSGA-II. Finally, a multi-objective problem of designing a vehicle-borne radar antenna pedestal is settled with the modified algorithm.

Evaluated Preference Genetic Algorithm and its Engineering Applications

2011 ◽  
Vol 467-469 ◽  
pp. 2129-2136
Author(s):  
Tung Kuan Liu ◽  
Hsin Yuan Chang ◽  
Wen Ping Wu ◽  
Chiu Hung Chen ◽  
Min Rong Ho
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Computation Complexity ◽  
Genetic Population ◽  
Multiobjective Genetic Algorithm ◽  
Multiobjective Optimization Problems ◽  
Engineering Problems ◽  
Objective Vector

This paper proposes a novel multiobjective genetic algorithm (MOGA), Evaluated Preference Genetic Algorithm (EPGA), for efficiently solving engineering multiobjective optimization problems. EPGA utilizes a preferred objective vector to perform a fast multiobjective ranking schema within a low computation complexity O(MNlogN) where N is the size of genetic population and M is the number of objectives. For verifying the proposed algorithms, this paper studies two engineering problems in which multiple mutual-conflicted objectives should be considered. According to the experimental results, the proposed EPGA can efficiently explore the Pareto front and provide very good solution capabilities for the engineering optimization problems.

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