MODE-LD&amp;#x002B;SS: A novel Differential Evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization

Montano, Alfredo Arias; Coello, Carlos A. Coello; Mezura-Montes, Efrén

doi:10.1109/cec.2010.5586137

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…In [6], five different radial basis functions (RBFs) with different basis functions were used as metamodels along with the EA MODE-LD+SS [5]. However, these metamodels were used independently for each objective function but individuals from each metamodel evaluation were used to update all metamodels as discussed next.…”

Section: Algorithms Based On Multiple Metamodelsmentioning

confidence: 99%

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A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

et al. 2017

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Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008-2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation based algorithms. We also compare these algorithms based on different criteria such as metamodelling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive mmultiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.

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Section: Algorithms Based On Multiple Metamodelsmentioning

confidence: 99%

“…The efficiency of different algorithms in terms of reducing computational cost or number of function evaluations is emphasized. 5. Various shortcomings in algorithms are observed and discussed e.g.…”

Section: Introductionmentioning

confidence: 99%

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

et al. 2017

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“…In order to make the evolution of subproblems in MOEA/D more effective, some effort has been made in the last several years. Zhang (2009), Zhang et al (2009), Montaño et al (2010) are some of them. In Li and Zhang (2009) a version combining DE operator and MOEA/D is proposed.…”

Section: Introductionmentioning

confidence: 98%

“…In Zhang et al (2009), a strategy of allocation of computational resource is designed to make the algorithm focus more attention on more promising subproblems. In Montaño et al (2010), the neighbors of one solution are determined with the Euclidean distance between solutions. The nondominated solution among its neighbors is defined as locally nondominated solution.…”

Section: Introductionmentioning

confidence: 99%

Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition

Wang

Jiao

2015

Soft Comput

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As an important multi-objective optimization algorithm, multi-objective evolutionary algorithm based on decomposition (MOEA/D) attracts more and more attention recently. In this paper, some methods inspired from quantum behavior are integrated in MOEA/D. A new algorithm, quantum-inspired MOEA/D (QMOEA/D), is proposed and proved to be effective to improve the performance of MOEA/D. In the new algorithm, a global solution (GS) and a local solution (LS) are stored for each subproblem. The attractor and characteristic length in quantum-inspired method are designed with GS and LS. The LS is selected as the attractor for each subproblem. And the characteristic length is associated with the difference between the LS and GS. The algorithm based on nondominated sorting is used for comparing firstly. Then the original and some advanced versions of MOEA/D are used as the comparison algorithms. Through the comparison it can be found that GS and LS are helpful to retain the diversity of the solutions. A wide Pareto front can be obtained on most of the test suites. And the quantum-inspired generator is effective to obtain better solutions with GS and LS.Keywords Multi-objective optimization · Quantuminspired method · Attractor · Characteristic length · MOEA/D

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“…DE has been proved to be the fastest evolution algorithm [4]. Solving MOPs with DE naturally attracted great attention and some DE-based MOEAs have been successively proposed, such as PDE [5] proposed by Abbass et al in 2001, PDEA [6] proposed by Madavan et al in 2002, DEMO [7] proposed by Robic et al in 2005.OW-MOSaDE [8] with learning strategies introduced proposed by Huang, V.L, MODE-LD+SS [9] combined with partial domination and invariant selection mechanism proposed by Alfredo in 2010, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Differential Evolution Algorithm based on Adaptive Mutation and Partition Selection

Zhao¹,

Zhang²,

Huang³

et al. 2013

JCP

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A multi-objective differential evolution algorithm based on adaptive mutation strategies and partition selected search is proposed based on classical differential evolution (DE) to further improve the convergence and diversity of multi-objective optimization problems. This algorithm improves mutation operation in DE, makes search oriented and ensures the convergence of algorithm by adaptively selecting mutation strategies based on the non-inferiority of the individuals of the population in evolution. In addition, a partition-based elitist preserving mechanism is applied to select the best individuals for the next generation, thus improving the selection operation in DE and maintaining the diversity of Pareto optimal set. The experiment on 5 ZDT test functions and 3 DTLZ test functions and comparison with and analysis of other classical algorithms such as NSGA-II and SPEA2 show that this algorithm converges the populations towards non-inferior frontier rapidly on the premise of maintaining the diversity of the populations. From the measure and graphs, it can be seen that this algorithm is feasible and effective in solving the multi-objective optimization problems.

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MODE-LD+SS: A novel Differential Evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization

Cited by 13 publications

References 25 publications

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition

Multi-objective Differential Evolution Algorithm based on Adaptive Mutation and Partition Selection

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