Calls for Papers (special): International Journal of Applied Evolutionary Computation (IJAEC)


Special Issue On: New, Hybrid and Improved Local and Global Searching Methods for Solving Complex Problems

Submission Due Date
8/31/2020

Guest Editors
Dr. Habib Shah, Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

Introduction
In computer and mathematical sciences, both local and global optimization methods have their own advantages and disadvantage respectively, one can use them both in the algorithm, while global version is used to get quick result and local version can refine the search space. Based on the improvement of single variable, hybridization, guided, merging can enhance and improve the performance of the given local or global search algorithm for solving complex problems. These methods can particularly increase the exploration and exploitation process in a balance way for achieving the optimal results of particular complex science, engineering problems. These proposed methods normally increase the efficiency and effectiveness as well in various domain of problems. Based on that, the researchers already focused how to improve the existence local and global search method through the proposed effective methodologies

Objective
This special issue aims to demonstrate how to solve hard computer science, mathematical and engineering problems by using the new, improved, hybrid local and global search algorithms in various applications such as classification, numerical function optimization, prediction, detection and so on. This issue seeks to publish unique and latest research and reflect the most recent advances and the latest contributions of optimization in the subject areas below, covering soft computing, Data mining, machine learning. The field of local and global optimization methods are vast, flexible, and interesting; therefore, the scope of this special issue has been kept wide.

Recommended Topics
• Simulated annealing and genetic optimization,
• Local gradient-based optimization,
• Steepest Descent
• Newton, Quasi-Newton, levenberg marquardt based methods
• Multiobjective Evolutionary Algorithm
• Tabu Search
• Single objective optimization
• Conjugate Gradient
• Guided Local and Global search methods
• Artificial Bee Colony
• Negative Selection Algorithm
• Bat Algorithm
• Machine Learning with Local and Global Learning Methods

Complex Optimization Problems:
• Variables
• Objective functions
• Constraints
• Discrete vs Continuous
• Available information
• Optimality
• Problem size
• Univariate and multivariate classification and prediction
• Travelling salesman problem

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on New, Hybrid and Improved Local and Global Searching Methods for Solving Complex Problems on or before 31, August, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Habib Shah
Guest Editor
International Journal of Applied Evolutionary Computation
E-mail: habibshah.uthm@gmail.com

Special Issue On: Emerging Superheuristic Learning Methods with Machine Learning for Solving Complex Problems

Submission Due Date
10/30/2020

Guest Editors
Dr. Wali Khan Mashwani, Institute of Numerical Sciences, Kohat University of Science &Technology, Kohat, KPK, 26000, Pakistan

Introduction
In the last two decades and so, metaheuristic which is also called Superheuristic methods has gained much popularity and very famous due to obtaining the effective and efficient simulations results for much complicated and hard problems posed in various fields of science and engineering technologies. These methods were also famous by growing interest in bio-inspired algorithms inspired from the natural intelligence behaviors of social insects such as ant, bees, birds and mammalia as well. On another side, Machine learning approaches have successfully applied to deal with hard problems especially in computer, engineering and mathematical science areas through the supervised, unsupervised and reinforcement methods. Based on the published research work, both machine learning and Superheuristic learning methods are robust as compared to typical methods. Both methods have their own qualitative benefits; one can use them both in the same problem. Researchers particularly from mathematical and computer sciences areas have improved these methods in different domain, rules and procedures for getting outstanding performance especially in data science and numerical problems. Therefore, how to train machine learning by latest and effective bio-inspired learning methods, are the main focus of researchers.

Objective
This special issue aims to propose the new, merge, improved and hybrid superheuristic learning methods along with Machine Learning to solve hard computer science, mathematical and engineering problems such as numerical function optimization, Time series, data analytics, financial and medical application and issues, classification and clustering. This special issue seeks to publish unique and latest research and reflect the most recent advances and the latest contributions of machine learning with Superheuristic training methods in the subject areas below, covering recent machine learning algorithms based on artificial bio-inspired agents. The field of Superheuristic Learning Methods with Machine Learning is very vast, and interesting; therefore, the scope of this special issue has been kept wide.

Recommended Topics
• Particle Swarm Optimization
• Differential Evolution
• Artificial Bee Colony, Ant colonies
• Firefly Algorithm
• Bat, Cuckoo and African Buffalo Algorithms
• Machine learning with improved Local and Global Learning Methods
• Classification, Clustering and forecasting
• Discrete vs Continuous problems
• Univariate and multivariate classification and prediction
• Travelling salesman problem
• Supervised, Unsupervised, Hybrid Learning algorithms

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Emerging Superheuristic Learning Methods with Machine Learning for Solving Complex Problems on or before 30, October, 2020. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Wali Khan Mashwani
Guest Editor
International Journal of Applied Evolutionary Computation (IJAEC)
E-mail: mashwanigr8@gmail.com; walikhan@kust.edu.pk