다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Black hole: A new heuristic optimization approach for data clustering

This paper presents a novel nature-inspired optimization paradigm, named as squirrel search algorithm (SSA). This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding. Gliding is an effective mechanism used by small mammals for travelling long distances. The present work mathematically models this behaviour to realize the process of optimization. The efficiency of the proposed SSA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test and ANOVA on classical as well as modern CEC 2014 benchmark functions. An extensive comparative study is carried out to exhibit the effectiveness of SSA over other well-known optimizers in terms of optimization accuracy and convergence rate. The proposed algorithm is implemented on a real-time Heat Flow Experiment to check its applicability and robustness. The results demonstrate that SSA provides more accurate solutions with high convergence rate as compared to other existing optimizers.

A novel nature-inspired algorithm for optimization: Squirrel search algorithm

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

A review: On path planning strategies for navigation of mobile robot

Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

A generalized type-2 fuzzy granular approach with applications to aerospace

Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.Performance of different ACO variants was observed to choose one as the basis to the proposed approach.Convergence fuzzy controller with the objective of maintaining diversity to avoid premature convergence was created. Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.

José Soria

Papers

Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot

Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

A generalized type-2 fuzzy granular approach with applications to aerospace

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

A Fuzzy Hierarchical Operator in the Grey Wolf Optimizer Algorithm