Complexity

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems


Publishing date
01 May 2018
Status
Published
Submission deadline
29 Dec 2017

1Budapest University of Technology and Economics, Budapest, Hungary

2University of Cadiz, Cadiz, Spain

3University of Alberta, Edmonton, Canada

4Sejong University, Seoul, Republic of Korea

5Murdoch University, Perth, Australia


Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Description

Models and procedures based on nature inspired approaches are called Computational Intelligence (CI). CI includes three major areas: fuzzy systems, artificial neural networks, and evolutionary (and population based) algorithms, including their hybrid combinations. It is often advantageous to combine them with traditional approaches, such as gradient type optimization or bounded exhaustive search. Such methods often successfully cope with complexity.

Computer Science (CS) defines complex systems as being intractable (usually NP-hard) and showing nondeterministic and uncertain behavior. Problems with complex systems are common in engineering, in natural sciences, in biomedical systems, and even in social systems. Modeling, control, decision support, search, and optimization here often lead to a dead end when classic mathematics and traditional CS are applied. From the application aspect, however, it is often sufficient to deploy approaches which offer good suboptimal solutions. Where no general algorithm exists, a “satisfactory solution” might be achieved by a proper metaheuristic, suitable for finding the exact optimum for a limited class of problems (with bounded dimensionality or bounded uncertainty) and delivering close to the optimum solution for a wider (still bounded) class of the same problem. The “goodness” of such a solution is measured by the “overall cost.”

It is possible to quantify the overall cost of a combined model and associated algorithm by adding the costs of time and space complexity to the cost of model errors. A “solution” (model or data structure and algorithm) is “efficient” if the overall cost is possibly low or is less than a predefined threshold. Often in search and optimization problems there are metaheuristics, which certainly find the exact solution for a limited size and may find the solution in a wider range, while in other cases (depending on structure and size) they will not find any solution at all. The need of resources is often unpredictable; thus it is never sure if a solution will be delivered at all. We expect “efficacious” solutions, which are efficient and predictable at the same time—and possibly generally applicable.

This special issue will focus on most recent research results on new models, structures, and algorithmic, mainly metaheuristic approaches to complex problems with reasonably good modeling accuracy and with reasonably low and predictable resources needed.

As CI approaches are often suitable for delivering such efficacious solutions, authors are invited to present theories, algorithms, and frameworks aimed at bringing about advanced techniques of Computational Intelligence for the modeling of complex systems and the solution of complex problems.

Potential topics include but are not limited to the following:

  • Interpolative and hierarchical fuzzy systems
  • Fuzzy signatures and signature sets
  • Fuzzy cognitive maps, artificial neural networks, and learning systems
  • Genetic, bacterial, and other evolutionary algorithms (Big Bang Big Crunch Algorithm, Particle Swarm Optimization, Imperialist Competitive Optimization, etc.)
  • Hybrid models consisting of fuzzy/neural/evolutionary algorithms
  • All in the context of complex systems and problems

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 7606715
  • - Editorial

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Laszlo T. Koczy | Jesus Medina | ... | Jin Hee Yoon
  • Special Issue
  • - Volume 2018
  • - Article ID 6360846
  • - Research Article

Simulations of Higher-Order Protein Organizations Using a Fuzzy Framework

B. Tüű-Szabó | L. T. Kóczy | M. Fuxreiter
  • Special Issue
  • - Volume 2018
  • - Article ID 1967398
  • - Research Article

An Improved Differential Evolution Algorithm for a Multicommodity Location-Inventory Problem with False Failure Returns

Congdong Li | Hao Guo | ... | Yu Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 4521672
  • - Research Article

Simulation-Based Optimization on the System-of-Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network-Centric Warfare

Bong Gu Kang | Seon Han Choi | ... | Tag Gon Kim
  • Special Issue
  • - Volume 2018
  • - Article ID 8395193
  • - Research Article

A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems

José García | Francisco Altimiras | ... | Oscar Peredo
  • Special Issue
  • - Volume 2018
  • - Article ID 2726070
  • - Research Article

A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain

Hao Guo | Congdong Li | ... | Yu Wang
  • Special Issue
  • - Volume 2018
  • - Article ID 7379512
  • - Research Article

Regression and ANN Models for Electronic Circuit Design

M. I. Dieste-Velasco | M. Diez-Mediavilla | C. Alonso-Tristán
  • Special Issue
  • - Volume 2018
  • - Article ID 1910520
  • - Research Article

Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction

Yuehjen E. Shao | Jun-Ting Dai
  • Special Issue
  • - Volume 2018
  • - Article ID 3671428
  • - Research Article

About Extracting Dynamic Information of Unknown Complex Systems by Neural Networks

Eloy Irigoyen | Antonio Javier Barragán | ... | José Manuel Andújar
  • Special Issue
  • - Volume 2018
  • - Article ID 5087851
  • - Research Article

Some Hesitant Fuzzy Linguistic Muirhead Means with Their Application to Multiattribute Group Decision-Making

Jun Wang | Runtong Zhang | ... | Borut Buchmeister
Complexity
 Journal metrics
Acceptance rate39%
Submission to final decision68 days
Acceptance to publication52 days
CiteScore2.690
Impact Factor2.591
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