Axioms

Special Issue Information

Dear Colleagues,

Mathematical optimization algorithms based on collective intelligence and its applications are a recent tool for solving complex optimization in computational intelligence.

Several algorithms have recently been developed in this area, such as particle swarm optimization, bat algorithm, ant colony optimization, bee colony, dolphin algorithm, wolf search, flower pollination algorithm, firefly, mayfly, ant colony, cuckoo search, termite colony, and cat swarm. However, determining how to design efficient methods and how to use these algorithms for real problems is still an open issue—in particular, in fuzzy logic systems, where if-then rules are used to represent the knowledge of the problems, mathematical optimization algorithms can be implemented for parameter adaptation in control systems. Also, neural networks have received some interest. However, in recent years, several mathematical models have been developed to optimize the architectures of the neural networks. In addition, new emerging neural models have recently been proposed. In all these models, a common problem is determining how to obtain an optimal topology, which can be handled by mathematical optimization algorithms.

This Special Issue invites researchers to report their latest research work on the development of new improved mathematical optimization algorithms, or new applications of existing methods in the design of topologies of neural models, parameter adaptation in control systems and path planning of robots, etc., with ultimate goal of exploring future research directions.

Potential themes include but are not limited to the following:

Theoretical methods for understanding the behavior of mathematical optimization algorithms;
Statistical approaches for understanding the behavior of mathematical optimization algorithms;
Optimization of neuro-fuzzy models;
Optimization of mathematical fuzzy logic models;
Optimization of emergent neural models with nature-inspired algorithms;
Mathematical fuzzy logic and intelligent and automatic control;
Mathematical bio-inspired algorithms.

Dr. Fevrier Valdez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

optimization algorithms
swarm intelligence
bio-inspired algorithms
fuzzy logic
neural networks
collective intelligence
mathematical algorithms

Published Papers (2 papers)

Download All Papers

Research

20 pages, 7083 KiB

Open AccessArticle

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

by Leticia Amador-Angulo and Oscar Castillo

Axioms 2024, 13(1), 33; https://doi.org/10.3390/axioms13010033 - 31 Dec 2023

Viewed by 1496

Abstract

In this paper, an improved whale optimization algorithm (WOA) based on the utilization of an interval type-2 fuzzy logic system (IT2FLS) is presented. The main idea is to present a proposal for adjusting the values of the

{\vec{r}}_{1}

and [...] Read more.

{\vec{r}}_{1}

and

{\vec{r}}_{2}

parameters in the WOA using an IT2FLS to achieve excellent results in the execution of the WOA. The original WOA has already proven itself as an algorithm with excellent results; therefore, a wide variety of improvements have been made to it. Herein, the main purpose is to provide a hybridization of the WOA algorithm employing fuzzy logic to find the appropriate values of the

{\vec{r}}_{1}

and

{\vec{r}}_{2}

parameters that can optimize the mathematical functions used in this study, thereby providing an improvement to the original WOA algorithm. The performance of the fuzzy WOA using IT2FLS (FWOA-IT2FLS) shows good results in the case study of the benchmark function optimization. An important comparative with other metaheuristics is also presented. A statistical test and the comparative with other bio-inspired algorithms, namely, the original WOA with type-1 FLS (FWOA-T1FLS) are analyzed. The performance index used is the average of the minimum errors in each proposed method. Full article

(This article belongs to the Special Issue Advances in Mathematical Optimization Algorithms and Its Applications)

► Show Figures

Figure 1

22 pages, 7897 KiB

Open AccessArticle

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

by Patricia Melin, Daniela Sánchez and Oscar Castillo

Axioms 2024, 13(1), 5; https://doi.org/10.3390/axioms13010005 - 20 Dec 2023

Cited by 1 | Viewed by 1687

Abstract

An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS. Full article

(This article belongs to the Special Issue Advances in Mathematical Optimization Algorithms and Its Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Advances in Mathematical Optimization Algorithms and Its Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI