Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks

• URL: http://arxiv.org/abs/2008.07094v1
• Date: Mon, 17 Aug 2020 05:28:20 GMT
• Title: Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks
• Authors: Lie Meng Pang, Hisao Ishibuchi and Ke Shang
• Abstract summary: We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.
• Score: 7.745468825770201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed. The existing EMO algorithms are mainly developed based on the final population framework. In the final population framework, the final population of an EMO algorithm is presented to the decision maker. Thus, it is required that the final population produced by an EMO algorithm is a good solution set. Recently, the use of solution selection framework was suggested for the design of EMO algorithms. This framework has an unbounded external archive to store all the examined solutions. A pre-specified number of solutions are selected from the archive as the final solutions presented to the decision maker. When the solution selection framework is used, EMO algorithms can be designed in a more flexible manner since the final population is not necessarily to be a good solution set. In this paper, we examine the design of MOEA/D under these two frameworks. We use an offline genetic algorithm-based hyper-heuristic method to find the optimal configuration of MOEA/D in each framework. The DTLZ and WFG test suites and their minus versions are used in our experiments. The experimental results suggest the possibility that a more flexible, robust and high-performance MOEA/D algorithm can be obtained when the solution selection framework is used.

Related papers

• Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank [8.421614560290609]
  This paper develops a framework for designing preference-based EMO algorithms to find solution(s) of interest (SOI) in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm.
  arXiv  Detail & Related papers  (2022-04-06T06:34:05Z)
• A Simple Evolutionary Algorithm for Multi-modal Multi-objective Optimization [0.0]
  We introduce a steady-state evolutionary algorithm for solving multi-modal, multi-objective optimization problems (MMOPs) We report its performance on 21 MMOPs from various test suites that are widely used for benchmarking using a low computational budget of 1000 function evaluations.
  arXiv  Detail & Related papers  (2022-01-18T03:31:11Z)
• Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees [94.72461292387146]
  We propose to reformulate the result diversification problem as a bi-objective search problem, and solve it by a multi-objective evolutionary algorithm (EA) We theoretically prove that the GSEMO can achieve the optimal-time approximation ratio, $1/2$. When the objective function changes dynamically, the GSEMO can maintain this approximation ratio in running time, addressing the open question proposed by Borodin et al.
  arXiv  Detail & Related papers  (2021-10-18T14:00:22Z)
• ES-Based Jacobian Enables Faster Bilevel Optimization [53.675623215542515]
  Bilevel optimization (BO) has arisen as a powerful tool for solving many modern machine learning problems. Existing gradient-based methods require second-order derivative approximations via Jacobian- or/and Hessian-vector computations. We propose a novel BO algorithm, which adopts Evolution Strategies (ES) based method to approximate the response Jacobian matrix in the hypergradient of BO.
  arXiv  Detail & Related papers  (2021-10-13T19:36:50Z)
• Algorithm Selection on a Meta Level [58.720142291102135]
  We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
  arXiv  Detail & Related papers  (2021-07-20T11:23:21Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)
• Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets [7.745468825770201]
  It is assumed that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations.
  arXiv  Detail & Related papers  (2020-12-14T08:04:07Z)
• Algorithm Configurations of MOEA/D with an Unbounded External Archive [7.745468825770201]
  We show that the performance of MOEA/D is improved by linearly changing the reference point specification during its execution. We also examine the use of a genetic algorithm-based offline hyper-heuristic method to find the best configuration of MOEA/D in each framework.
  arXiv  Detail & Related papers  (2020-07-27T08:14:37Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission Planning Problems [2.191505742658975]
  Some problems have many objectives which lead to a large number of non-dominated solutions. This paper presents a new algorithm that has been designed to obtain the most significant solutions. This new algorithm has been applied to the real world application in Unmanned Air Vehicle (UAV) Mission Planning Problem.
  arXiv  Detail & Related papers  (2020-02-20T17:07:08Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.