Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

Abstract

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems

“…We consider two measures of experimental comprehensiveness: the number of combinations and the range of each parameter. The number of examined 𝑛 𝑡 -𝜏 𝑡 pairings is varied in recent works including one [20,32], two [23,36], three [13,22,38,43] and five [21]. Only the recent work by Zhang et al [37] considers a more diverse set with six different frequency-severity pairings: 𝑛 𝑡 -𝜏 𝑡 = {5 − 5, 5 − 10, 5 − 20, 10 − 5, 10 − 10, 10 − 20}.…”

“…Some of the principal benchmark dynamic multi-objective optimization problems (DMOPs) were defined nearly two decades ago by Farina et al [12]. Since then, a plethora of benchmark problems have been proposed [8,13,17,19,20,22,32,33,[36][37][38], allowing for the testing of different characteristics of real world systems in a controllable environment. Dynamic Optimization Problem (DOP) generators such as Moving Peaks [4,36] and the Dynamic XOR [35] and others are well-known single-objective environments in which the difficulty of a problem instance can be controlled by increasing the number of peaks or bits respectively.…”

Dynamic multi-objective evolutionary algorithm based on decomposition with hybrid prediction

“…In predictive approaches, Miao et al [9] enhanced the accuracy of predicting the movement location of PS by clustering the population into multiple delegate groups using a proposed strategy. They also introduced a multidirectional prediction strategy to enhance the performance of the Evolutionary Algorithm (EA) in addressing DMOPs [10] .…”

Enhancing Decomposition Approach for Solving Multi-Objective Dynamic Non-Linear Programming Problems Involving Fuzziness

“…For fuzzy MONLP situations, Osman and El-Banna [29] proposed a qualitative analysis and stability. There are many researchers who have developed MODP (for instance, Moghaddam and Ghoseiri [30]; Muruganantham et al [31]; Li et al [32]; Deng et al [33]; Besheli et al [34]; Peraza et al [35]; Azevedo et al [36]; Ni et al [37]; Wu et al [38]; Liu et al [39]; Zou et al [40]; and Zhang et al [41]).…”