A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-kno...

A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

Dynamic optimization has become an increasingly important aspect of chemical processes in the past few decades. To solve such chemical dynamic optimization problems (DOPs) effectively, we put forward a modified differential evolution algorithm named XADE in this paper, which integrates the self-adaptive principle and multiple mutation strategies. In XADE, four mutation strategies with different characteristics are introduced instead of using a single strategy. Meanwhile, the mutation strategies and DE’s two control parameters are gradually adjusted adaptively based on the knowledge learned from the previous searches in generating improved solutions. The advantageous performance of XADE is validated by comparisons with several state-of-the-art adaptive DE variants on 24 complex test instances. Experimental results show that XADE is an effective approach to solving global numerical optimization problems. Moreover, the effectiveness of XADE is validated by applying the approach to 4 real-world complex DOPs with different characteristic in the chemical engineering field.

Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

A two-step method for the solution of dynamic optimization problems with state inequality constraints using iterative dynamic programming (IDP) is presented. In the first step, a preliminary control policy along with approximate values for the start and end points of the state-constrained arc is obtained. The final solution is obtained in the second step by using the preliminary control policy as an input along with an analytical expression for the control during the constrained portion of the state trajectory. The use of flexible stage lengths in IDP helps in accurately locating the start and end points of the state-constrained arc. For the examples considered, the method results in better values of the performance index than those previously reported in the literature. It also results in a significant reduction in computation time as compared to the existing method for the solution of problems with a single active state constraint and a single control using IDP.

Two-Step Method for Dynamic Optimization of Inequality State Constrained Systems Using Iterative Dynamic Programming

Optimal control problems with multiple conflicting objectives in chemical processes are quite challenging To solve such problems, we put forward a multistrategy-based multiobjective differential evolution, in which (1) a hybrid selection strategy is incorporated from the motivation of no single strategy outperforming all other ones in every stage; (2) a multipopulation strategy is applied to represent the main population and current optimum, and a cyclic crowding estimation is developed to maintain these optimum; and (3) a multimutation strategy is constructed to improve both exploration and exploitation ability The effectiveness and efficiency of the proposed algorithm are validated by comparisons with some representative multiobjective evolutionary algorithms over 12 test instances Moreover, the proposed algorithm is applied to solve 3 multiobjective optimal control problems in chemical processes The obtained results indicate the efficiency and effectiveness of the proposed algorithm for solving multiobjective optimal control problems

/pdf/a-multistrategy-based-multiobjective-differential-evolution-haid3d0449.pdf

A Multistrategy-Based Multiobjective Differential Evolution for Optimal Control in Chemical Processes

A modified harmony search algorithm with co-evolutional control parameters (DEHS), applied through differential evolution optimization, is proposed. In DEHS, two control parameters, i.e., harmony memory considering rate and pitch adjusting rate, are encoded as a symbiotic individual of an original individual (i.e., harmony vector). Harmony search operators are applied to evolving the original population. DE is applied to co-evolving the symbiotic population based on feedback information from the original population. Thus, with the evolution of the original population in DEHS, the symbiotic population is dynamically and self-adaptively adjusted, and real-time optimum control parameters are obtained. The proposed DEHS algorithm has been applied to various benchmark functions and two typical dynamic optimization problems. The experimental results show that the performance of the proposed algorithm is better than that of other HS variants. Satisfactory results are obtained in the application.

Harmony search algorithm with differential evolution based control parameter co-evolution and its application in chemical process dynamic optimization

To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process...

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jun Zhu

Author Tools

Chat about Author

Papers

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy