Background

In their original versions, evolutionary algorithms (EAs) and Swarm Intelligence (SI) approaches lack a mechanism to handle the constraints of an optimization problem. On the other hand, several real-world optimization problems include different types of constraints (e.g. linear, nonlinear, equality and inequality constraints). Therefore, a considerable amount of research has been dedicated to develop mechanisms to incorporate feasibility information into the fitness function of an EA. The most popular technique is the use of penalty functions. The aim is to decrease the fitness of those infeasible solutions in order to favor feasible solutions in the selection process. The main drawback of penalty functions is the careful fine-tuning required by the penalty factors, which determine the severity of the penalization.

Besides penalty functions, there are several constraint-handling mechanisms proposed in the specialized literature. However, it is an open problem how to choose the most adequate for a given search algorithm to solve a specific constrained optimization problem.

Mission

The mission of the task force is to encourage research efforts focused on the study and design of novel constraint-handling techniques, their synergies with different nature-inspired search algorithms and the solution of challenging real-world problems.

Topics

The topics expected to be tackled by the activities of the Task Force are the following:

• Novel constraint-handling techniques.
• Novel/adapted search-engines for constrained optimization.
• Mechanisms to balance the explorative and exploitative ability of EAs and SI and memetic algorithms in constrained search spaces.
• Parameter adaptation in constrained optimization.
• Novel algorithms for dealing with problems with mixed (including integer, discrete, and binary) variables.
• Theoretical analysis and complexity of algorithms in constrained optimization.
• Performance evaluation of algorithms in constrained optimization.
• Constraint-handling techniques in multiobjective optimization.
• Design of difficult and scalable test functions.
• Real-world applications.

Members

• Alfredo Arias-Montaño, CINVESTAV-IPN, MEXICO
• Mithun M. Bhaskar, National Insititute of Technology, INDIA
• Janez Brest, University of Maribor, SLOVENIA
• Eduardo G. Carrano, Centro Federal de Educação Tecnológica de Minas Gerais, BRAZIL
• Nareli Cruz-Cortés, CIC-IPN, MEXICO
• Rituparna Data, Korea Advanced Institute of Science and Technology KAIST · Elec, SOUTH KOREA
• Ricardo Landa-Becerra, CINVESTAV-Tamaulipas, MEXICO
• Ángel E. Muñoz-Zavala, Universidad Autónoma de Aguascalientes, MEXICO.
• Edgar Alfredo Portilla-Flores, CIDETEC-IPN, MEXICO.
• Tapabrata Ray, University of New South Wales at Australian Defence Force Academy, AUSTRALIA
• Setsuko Sakai, Hiroshima Shudo University, JAPAN
• Hemant K. Singh, University of New South Wales, AUSTRALIA
• Tetsuyuki Takahama, Hiroshima City University, JAPAN
• Gregorio Toscano-Pulido, CINVESTAV-Tamaulipas, MEXICO
• Yong Wang, Central South University (CSU), CHINA
• Elizabeth F. Wanner, Centro Federal de Educação Tecnológica de Minas Gerais, BRAZIL

• 2019 Special Session on Nature-Inspired Constrained Optimization at IEEE Congress on Evolutionary Computation (CEC'2019).

• 2019 Tutorial on Nature-Inspired Dynamic Constrained Optimization at IEEE Congress on Evolutionary Computation (CEC'2019).