Multiobjective Multifactorial Operation Optimization for Continuous Annealing Production Process

Abstract

To deal with the fluctuations of strip quality, low production capacity, and excessive energy consumption in continuous annealing production line (CAPL), a new multiobjective multifactorial operation optimization (MO-MFOO) model is established for the CAPL in this paper. Different from previous multiobjective operation optimization (MOO) models in literature that only solve one optimization task at a time, the MO-MFOO in this paper can simultaneously solve multiple optimization tasks, each of which is a MOO. To efficiently solve this model, we propose an adaptive multiobjective, multifactorial differential evolution algorithm (AdaMOMFDE), in which multiple mutation operators are adopted and an adaptive selection strategy for these operators is designed according to their search results to accelerate the convergence speed and improve the robustness. Experimental results on benchmark problems show that AdaMOMFDE is better than the traditional multiobjective multifactorial evolutionary algorithm in the literature. Moreover, experimental results on the practical problems illustrate that the MO-MFOO model based on AdaMOMFDE is superior to the traditional multiobjective operation optimization model that only optimizes one task at a time.

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

“…Applying different mutation operators on current population can generate different search directions and offspring populations. Multiple commonly-used mutation strategies (DE/rand/1, DE/best/1, DE/current-to-rand/1, DE/current-to-best/1, DE/rand/2, DE/best/2, and DE/best/1 + ρ) were investigated to accelerate the convergence speed in [23,115,149], where DE/best/1 + ρ is defined as follows:…”

“…Its rationale is that the current-found best solution is utilized adequately to guide the search to promising areas in the early phase, while an increased perturbation is also integrated subsequently for a diverse exploration [149]. Note that we selected the suitable mutation strategy randomly in [115] or adaptively according to their success rates in previous generations in [23].…”

“…AdaMOMFDE [23], MFEA/D-DRA [128] Inter-domain path computation under domain uniqueness constraint (IDPC-DU) MFEA [71] Optimal power flow (OPF) problem MFEA [177] Electric power dispatch problem MO-MFO [178] Well location optimization problem AT-MFEA [116] Operation optimization of integrated energy system MO-MFEA-II [121] Car structure design optimization problem Multifactorial PSO-FA hybrid algorithm [91], TS+FM [ Evolutionary algorithms often lose their effectiveness and efficiency when applied to large-scale optimization problems. Feng et al [111] presented a primary trial of solving large-scale optimization (up to 2000 dimensions) via the evolutionary multi-task assisted random embedding method.…”

“…Some environmental parameters and control variables have coupling relationships, which makes it difficult to achieve global optimization with traditional EAs. Wang and Wang [23] proposed an AdaMOMFDE algorithm based on the search mechanism of differential evolution. The optimal operation of integrated energy systems (IES) is of great significance to facilitate the penetration of distributed generators and then improve its overall efficiency.…”

“…Following from the formalization, the conceptual realizations of this paradigm are classified into three distinct categories, namely sequential transfer optimization, multi-task optimization (MTO), the main focus of this article, and multiform optimization. Note that the concept of multi-task optimization is also described using other terms such as multifactorial optimization (MFO) [18], multitasking optimization (MTO) [19], multi-task learning (MTL) [20], multitask optimization (MTO) [11], multitasking [12], evolutionary multitasking (EMT) [21], evolutionary multitasking (EMT) [22], and multifactorial operation optimization (MFOO) [23].…”