scholarly journals Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 122
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Natural Phenomena ◽  
Algorithm Development ◽  
Real World Applications ◽  
Nature Inspired Algorithms ◽  
Relationship Of

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.

scholarly journals An Exhaustive Review of Bio-Inspired Algorithms and its Applications for Optimization in Fuzzy Clustering

2021 ◽  
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Clustering ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Natural Phenomena ◽  
Algorithm Development ◽  
Complex Problems ◽  
Nature Inspired Algorithms ◽  
Relationship Of

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problem. These algorithms have been compared with the traditional ones algorithms and have demonstrated to be superior in complex problems. This paper attempts to describe the algorithms based on nature, that are used in fuzzy clustering. We briefly describe the optimization methods, the most cited nature-inspired algorithms published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new are of nature and bio-inspired optimization of fuzzy clustering.

A Survey of Recent Variants and Applications of Antlion Optimizer

2021 ◽  
Vol 10 (2) ◽  
pp. 48-73
Author(s):  
Shail Dinkar ◽  
Kusum Deep
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Optimization Problems ◽  
Environmental Engineering ◽  
Metaheuristic Algorithm ◽  
Natural Phenomena ◽  
Communication Engineering ◽  
Real World Applications ◽  
Working Principle ◽  
Complex Optimization

This work proposes a review of a recently developed swarm intelligence-based metaheuristic algorithm called Antlion Optimizer (ALO), its variants, and applications. The suitable blending of a random walk with an adaptive shrinking of hypersphere radius makes this algorithm more effective and impressive over other recent optimization algorithms. This paper elaborates on the recent variants of ALO by reviewing the concerned publications. It also summarized the applications of ALO for solving real-world complex optimization problems of a wide variety of areas. So, this paper comprises of summarized review of various recently published ALO papers. Firstly, the natural phenomena of ALO and the working principle of its various operators are described. Then the recently developed variants of ALO are described in detail depicting in various categories. The real-world applications using ALO and its variants are also described under global optimization, power and system engineering, electronics and communication engineering, machine learning, environmental engineering, and networking.

Editorial — Recent Progress in Intelligent and Evolutionary Algorithms and their Applications

2015 ◽  
Vol 11 (02) ◽  
pp. 115-120
Author(s):  
Aki-Hiro Sato ◽  
Hiroshi Kawakami ◽  
Toshihiro Hiraoka
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Recent Progress ◽  
Optimization Problems ◽  
Asia Pacific ◽  
Topical Issue ◽  
Special Issue ◽  
Mathematical Methods ◽  
Evolutionary Systems ◽  
Real World Applications

This is a topical issue on the 16th Asia–Pacific Symposium on Intelligent and Evolutionary Systems (IES) which was held in Kyoto from December 12–14, 2012. This special issue contains six articles related to evolutionary algorithms that are designed to solve optimization problems, network concepts, mathematical methods and their real world applications.

scholarly journals Analyses and Optimization Methods of Legged Robots: A Review

2020 ◽  
Author(s):  
Venkata Sainaveen Nandam ◽  
Praveen Seelaboyina ◽  
Sandeep Chowdary Kodavati ◽  
Harikrishna Molleti
Keyword(s):  
Real World ◽  
Present Status ◽  
Optimization Methods ◽  
Legged Robots ◽  
Review Of Literature ◽  
Energy Usage ◽  
Biologically Inspired ◽  
Real World Applications ◽  
Points Of View

During the most recent years, a lot of research has been done in creating robots with more self-governance so that they can overcome the challenges that real world environments present. The robot's limited versatility in real world applications can be overcome by the development of Legged robots. Also, as they permit movement in unavailable territory to robots with wheels, Legged Robots are more advantageous. But the potency of the legged robots explicitly its energy usage among alternate points of view really fall behind robots that use wheels. So, the present status of development, there are as yet a few perspectives that need to be analysed, optimized and enhanced. This paper presents review of literature of various biologically inspired legged robots, various techniques adopted for their analysis and optimization and the analysis and optimization of the ones that are not biologically inspired

scholarly journals A repository of real-world datasets for data-driven evolutionary multiobjective optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Cheng He ◽  
Ye Tian ◽  
Handing Wang ◽  
Yaochu Jin
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Data Driven ◽  
Benchmark Problems ◽  
Computational Costs ◽  
Evolutionary Multiobjective Optimization ◽  
Multiobjective Optimization Problems ◽  
Real World Applications ◽  
Real World Datasets

Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

scholarly journals A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Approach ◽  
Global Numerical Optimization ◽  
Hybrid Metaheuristic ◽  
Real World Applications

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

scholarly journals THE EFFICIENCY ANALYSIS OF MULTI-AGENT OPTIMIZATION METHODS OF AIRCRAFT DESIGNS ELEMENTS

2019 ◽  
Vol 22 (2) ◽  
pp. 96-108
Author(s):  
A. V. Panteleev ◽  
M.M. S. Karane
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Metaheuristic Algorithms ◽  
Great Success ◽  
Structural Elements ◽  
Development Environment ◽  
Fish School ◽  
Krill Herd ◽  
Multi Agent ◽  
The Given

The article considers the use of three multi-agent methods for optimizing structural elements of aircraft. The research describes strategies for finding solutions to multi-agent metaheuristic algorithms, such as: fish school search, krill herd, and imperialist competition algorithm. The work of these methods is based on the processes occurring in an environment that features many agents. Agents have the opportunity to exchange information in order to find a solution to the problem. These methods allow you to find an approximate solution, but, nevertheless, with great success are used in practice. In this regard, the described metaheuristic algorithms were applied to the optimization problems of structural elements of aircraft such as: welded beam, high pressure vessel, gearbox and tension spring. The article adduces the formulation of these problems: the objective function, a set of constraints and a set of admissible solutions are indicated, recommendations on the choice of parameters of the methods used are given. To solve the problems of optimizing the elements of aircraft construction, a set of software elements was formed in the development environment of Microsoft Visual Studio in C #. This complex of programs allows you to solve the given problems by each of the described multi-agent methods. The software allows you to select a method, a task and select the method parameters and the penalty function coefficients in the best possible way. The results of the solution were compared with each other and with the well- known solution. According to the numerical results of solving these tasks, we can conclude that the algorithmic and software created allow us to find a solution close to the exact one in a reasonable time.

Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering

2015 ◽  
Vol 24 (03) ◽  
pp. 1550003 ◽  
Author(s):  
Armin Daneshpazhouh ◽  
Ashkan Sami
Keyword(s):  
Intrusion Detection ◽  
Outlier Detection ◽  
Fuzzy Clustering ◽  
Real World ◽  
State Of The Art ◽  
Real Data ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Real World Applications

The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, some reliable negative instances are extracted by a kNN-based algorithm. Afterwards, fuzzy clustering using both negative and positive examples is utilized to detect outliers. Experimental results on real data sets demonstrate that the proposed approach outperforms the previous unsupervised state-of-the-art methods in detecting outliers.

scholarly journals A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

2021 ◽  
Author(s):  
Fei Ming
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Density Estimation ◽  
Real World ◽  
Selection Pressure ◽  
Optimization Problems ◽  
Variable Number ◽  
Objective Functions ◽  
Considerable Research ◽  
Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

scholarly journals Nature-Inspired Algorithms for Real-World Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-2
Author(s):  
Wei Fang ◽  
Xiaodong Li ◽  
Mengjie Zhang ◽  
Mengqi Hu
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Nature Inspired Algorithms
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