Forthcoming Special Issues
Recent Advances in Constraint Handling Techniques for Solving Constrained Optimization Problems
Over the past few decades, the emergence of the swarm and evolutionary algorithms has been a significant breakthrough in solving a diverse range of optimization problems. Although these techniques are popular, they do have inherent limitations, such as these are unconstrained search techniques. Research has been conducted regarding mechanisms that permit algorithms to handle equality and inequality constraints; both kinds of constraints can be linear or non-linear, depending on their structure. Therefore, the integration of these mechanisms into the algorithms' framework is a research area that is open for discussion.
Special issue information:
An optimization problem is a problem that is solved numerically by determining the variables that should be used during the minimization/maximization of objective functions while satisfying linear and/or non-linear constraints in the decision space. In many real-world applications, the objective functions and constraints have non-linearity with multiple local optimum locations with a smaller feasible range of values. Due to the presence of many local optima and, at the same time, the small size of the feasible region, solving these problems using classical algorithms is difficult and time-consuming. For the past three decades, Swarm-based Algorithms (SAs) and Evolutionary Algorithms (EAs) have been drawing considerable attention and have become a popular choice to solve challenging optimization problems that arise in the real world. The main advantage of these algorithms over classical mathematical programming is that they do not rely on explicit gradient information to solve optimization problems. They only use the objective function and constraints (if available) to solve the optimization problems. Thus, these algorithms can also be used to solve non-linear and non-convex problems with a discrete decision space. Furthermore, the stochastic behaviors of these algorithms' individuals (candidate solutions) provide a method for enabling global searches.
The majority of optimization problems involve non-linear and non-convex constraints, which in turn may degrade the robustness and effectiveness of any optimization algorithm since the volume of the feasible region in the decision variable space is smaller. It should be noted that most of the algorithms are inherently designed to deal with unconstrained optimization problems. Therefore, to account for the constraints of real-world problems, it is necessary to add an additional mechanism called Constraint Handling Technique (CHT). There have been a number of CHTs proposed in the literature. However, designing these CHTs for single-objective and multi-objective constrained optimization problems is still an open research area.
Scope and topics
This special issue focuses on recent advances in CHTs for solving constrained optimization problems. This special issue aims to provide a platform for researchers, industry professionals, academicians, and individuals working in these areas to gather together and exchange ideas and the latest findings. We encourage original papers to be submitted on topics of interest that include, but do not limit themselves to, the following:
- Single-objective constrained optimization
- Multi- and Many-objective constrained optimization
- Dynamic constrained optimization
- Multimodal constrained optimization
- Robust constrained optimization
- Bilevel constrained optimization
- Large-scale constrained optimization
- Application of CHTs on hard to solve Real-world problems from different Domains.
- Surrogate-assisted constrained optimization
- Bound-constraint handling techniques.
- Rule-based CHTs
- Self-Adaptive Penalty Functions
- Repair Methods
- Stochastic Ranking
- Multi-objective approach to handle constraints.
- Ensembles of different CHTs
- Variants of different constrained Methods such as alpha-constrained and epsilon-constrained methods.
Manuscript submission information:
Important Dates
Date first submission expected: 01-Oct-2022
Final deadline for submission: 31-Mar-2023
Final deadline for acceptance: 31-Jan-2024
The manuscripts should be prepared according to the “Guide for Authors” section of the journal found at: https://www.elsevier.com/journals/swarm-and-evolutionary-computation/2210-6502/guide-for-authors/ and submission should be done through the journal’s submission website: https://www.evise.com/profile/#/SWEVO/login/ by selecting “VSI: Recent CHTs ” and also clearly indicating the full title of this special issue “Recent Advances in Constraint Handling Techniques for Solving Constrained Optimization Problems” in comments to the Editor-in-Chief.
Each submitted paper will be reviewed by expert reviewers. Submission of a paper will imply that it contains original unpublished work and is not being submitted for publication elsewhere.
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
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