multiobjective evolutionary algorithm based on mixture

Latest Stored Information Based Adaptive Selection

The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D) named latest stored information based adaptive selection (LSIAS)

A Survey of Multiobjective Evolutionary Algorithms Based

multiobjective evolutionary algorithms based on decomposition (MOEA/D) to tackle these complex opti-mization problems ef˝ciently Therefore MOEA/D has found wide applications in various ˝elds and been attracting increasingly signi˝cant attention from both academia and industry since it

An adaptive multiobjective estimation of distribution

Aug 27 2016Zhou A Zhang Q Zhang G (2014) Multiobjective evolutionary algorithm based on mixture gaussian models J Softw 25(5):913–928 MathSciNet zbMATH Google Scholar Zhou A Zhang Y Zhang G Gong W (2015) On neighborhood exploration and subproblem exploitation in decomposition based multiobjective evolutionary algorithms

Hiding sensitive itemsets with multiple objective

Several evolutionary algorithms were introduced to minimize those three side effects of PPDM using a single-objective function that generates one solution for sanitization This paper presents a multiobjective algorithm (NSGA2DT) with two strategies for hiding sensitive information with transaction deletion based on the NSGA-II framework

PSA Based Multi Objective Evolutionary Algorithms

Bosman P A N Thierens D : Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms International Journal of Approximate Reasoning 31(3) 259–289 (2002) MathSciNet zbMATH CrossRef Google Scholar

Scalarizing Functions in Decomposition

effect in decomposition-based multiobjective evolutionary algo-rithms Additionally we come up with an efficient framework for decomposition-based multiobjective evolutionary algorithms based on the proposed scalarizing functions and some new strategies Extensive experimental studies have demonstrated the

Minimizing risk on a fleet mix problem with a

A Lausch S Wesolkowski Matching Air Mobility Tasks to Platforms: Preliminary Algorithm and Results CORA Report 2009 Google Scholar M Mazurek S Wesolkowski Fleet Mix Computation Using Evolutionary Multiobjective Optimization IEEE Computational Intelligence Symposium on Multi-criteria Decision Making Nashville TN 2009 Google

Multiobjective Di erential Evolution Enhanced with

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems Many of them adopt mutation and crossover operators from di erential evolution However these operators do not explicitly utilise features of

Reliability

These techniques are coupled in various ways with optimization in the classical reliability-based optimization field This paper demonstrates how classical reliability-based concepts can be borrowed and modified and with integrated single and multiobjective evolutionary algorithms used to enhance their scope in handling uncertainties involved

Figure 2 from Improved Regularity Model

Fig 2 2-D example to illustrate the motivation of mapping the reference vectors Specifically there are nine reference vectors (blue lines) generated by the Das and Dennis's method and line AB denotes the Pareto front (a) Reference vectors without mapping are plotted in which only four reference vectors intersect AB while the (b) reference vectors which have been mapped are plotted in

A Multipopulation

Abstract: Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems In this paper a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations

An adaptive multiobjective estimation of distribution

Aug 27 2016Zhou A Zhang Q Zhang G (2014) Multiobjective evolutionary algorithm based on mixture gaussian models J Softw 25(5):913–928 MathSciNet zbMATH Google Scholar Zhou A Zhang Y Zhang G Gong W (2015) On neighborhood exploration and subproblem exploitation in decomposition based multiobjective evolutionary algorithms

An Adaptive Quantum

3 Rules of Adaptive Quantum-based Multiobjective Evolutionary Algorithm An adaptive quantum-based multiobjective algorithm AQMEA utilizes a new representation called a Q-bit for the probabilistic representation that is based on the concept of qubits [20] A qubit is a two-layer quantum system that can be modeled as the Hilbert space H 2

An Investigation of Generalized Differential Evolution

The multiobjective evolutionary metaheuristics are population based techniques for solving complex multiobjective optimization problems A metaheuristic is an iterative master process that guides and modifies the operation of a subordinate heuristic to efficiently produce high quality solutions by exploring and exploiting a solution search

l IEEE l (CCF) l Swarm and Evolutionary Computation l Complex Intelligent Systems l IEEE Transactions on Evolutionary Computation IEEE Transactions on Cybernetics IEEE Computational Intelligence Magazine Pattern Recognition Information Sciences CEC GECCO EMO IJCAI AAAI NeurIPS

Multiobjective Simulated Annealing: A Comparative Study to

evolutionary algorithms Evolutionary algorithms (EAs) have many interesting properties and have been widely used in various optimization problems from combinatorial problems such as job shop scheduling to real valued parameter optimization [2 3] Also many evolutionary algorithms for solving the multiobjective problemhave been suggested [19 20

Adaptive Operator Selection With Bandits for a

Jan 11 2013Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously Thus it is natural and feasible to use AOS in MOEA/D We investigate several important issues in using FRRMAB in MOEA/D

Global WASF

Jun 01 2017In this article we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA (global weighting achievement scalarizing function genetic algorithm) which falls within the aggregation-based evolutionary algorithms The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front

A Hybrid Simplex Multi

A Hybrid Simplex Multi-Objective Evolutionary Algorithm Based on A New Fitness Assignment Strategy Xiaofang Guo Yuping Wang School of Computer Science and Technology Xidian University Xi'an 710071 China Email: {gxfang1981126 ywangxidian edu cn} Abstract—In multi-objective evolutionary algorithms

A Hybrid Simplex Multi

A Hybrid Simplex Multi-Objective Evolutionary Algorithm Based on A New Fitness Assignment Strategy Xiaofang Guo Yuping Wang School of Computer Science and Technology Xidian University Xi'an 710071 China Email: {gxfang1981126 ywangxidian edu cn} Abstract—In multi-objective evolutionary algorithms

Multiobjective Immune Algorithm with Nondominated

artificial immune system algorithm MISA (Coello Coello and Cortes 2002 2005) based on the clonal selection principle (Burnet 1959) to solve multiobjective optimization problems We also proposed an immune algorithm IDCMA (Jiao et al 2005) which is the groundwork for this paper Both the two algorithms adopted binary representation

Multiobjective Cloud Particle Optimization Algorithm Based

Multiobjective Evolutionary Algorithm Based on Decomposition MOEA/D [17] is a new framework for solving multi -objective optimization problems It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously In MOEA/D the weighted sum approach the Tchebycheff approach

A Mixed Representation

A Mixed Representation-Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection Abstract: Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently However most of the existing methods focus on addressing the task of

Multiobjective Immune Algorithm with Nondominated

artificial immune system algorithm MISA (Coello Coello and Cortes 2002 2005) based on the clonal selection principle (Burnet 1959) to solve multiobjective optimization problems We also proposed an immune algorithm IDCMA (Jiao et al 2005) which is the groundwork for this paper Both the two algorithms adopted binary representation

Figure 2 from Improved Regularity Model

Fig 2 2-D example to illustrate the motivation of mapping the reference vectors Specifically there are nine reference vectors (blue lines) generated by the Das and Dennis's method and line AB denotes the Pareto front (a) Reference vectors without mapping are plotted in which only four reference vectors intersect AB while the (b) reference vectors which have been mapped are plotted in

Indicator

For over 25 years most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance However the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems) mainly because of the loss of

An Interactive Evolutionary Metaheuristic for

Multiobjective evolutionary algorithms based on target region preferences Swarm and Evolutionary Computation Vol 40 PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

Multiobjective Simulated Annealing: A Comparative Study to

evolutionary algorithms Evolutionary algorithms (EAs) have many interesting properties and have been widely used in various optimization problems from combinatorial problems such as job shop scheduling to real valued parameter optimization [2 3] Also many evolutionary algorithms for solving the multiobjective problemhave been suggested [19 20

Multiobjective Reservoir Optimization Using Genetic

a multiobjective differential evolution algorithm (MODE) for optimization of a multiobjective reservoir system operation and compared it with genetic algorithm NSGA-II using some test cases Afsharian Zadeh [3] used particle swarm optimization (PSO) for reliability based optimal