scholarly journals Credibilistic fuzzy clustering based on evolutionary method of crazy cats

2021 ◽  
pp. 110-119
Author(s):  
Yevgeniy Bodyanskiy ◽  
Alina Shafronenko ◽  
Iryna Pliss
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Clustering ◽  
High Speed ◽  
Random Search ◽  
Goal Function ◽  
Large Datasets ◽  
Global Extremum ◽  
Mathematical Procedure ◽  
Global Random Search ◽  
Search Modes

The problem of fuzzy clustering of large datasets that are sent for processing in both batch and online modes, based on a credibilistic approach, is considered. To find the global extremum of the credibilistic fuzzy clustering goal function, the modification of the swarm algorithm of crazy cats swarms was introduced, that combined the advantages of evolutionary algorithms and a global random search. It is shown that different search modes are generated by a unified mathematical procedure, some cases of which are known algorithms for both local and global optimizations. The proposed approach is easy to implement and is characterized by the high speed and reliability in problems of multi-extreme fuzzy clustering.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

Cascaded Hidden Space Feature Mapping, Fuzzy Clustering, and Nonlinear Switching Regression on Large Datasets

2018 ◽  
Vol 26 (2) ◽  
pp. 640-655 ◽  
Author(s):  
Jun Wang ◽  
Huan Liu ◽  
Xiaohua Qian ◽  
Yizhang Jiang ◽  
Zhaohong Deng ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Large Datasets ◽  
Switching Regression ◽  
Feature Mapping ◽  
Nonlinear Switching

Stopping rules in k-adaptive global random search algorithms

2010 ◽  
Vol 48 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Anatoly Zhigljavsky ◽  
Emily Hamilton
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Stopping Rules ◽  
Global Random Search

scholarly journals On the Hardness of Offline Multi-objective Optimization

2007 ◽  
Vol 15 (4) ◽  
pp. 475-491 ◽  
Author(s):  
Olivier Teytaud
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Random Search ◽  
Computational Cost ◽  
Multiobjective Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Insight Into ◽  
Better Than

It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.

scholarly journals Development of an algorithm for optimization of continuous technological process of cultivation of microorganisms

2021 ◽  
Vol 264 ◽  
pp. 04032
Author(s):  
Sherkul Rakhmanov ◽  
Rano Gaziyeva ◽  
Dilbaroy Abdullaeva ◽  
Nigora Azizova
Keyword(s):  
Random Search ◽  
Control Object ◽  
Goal Function ◽  
Economic Indicators ◽  
Technological Processes ◽  
Operational Forecasting ◽  
Absolute Bias ◽  
Cultivation Process ◽  
Almost All ◽  
Technical And Economic Indicators

When implementing the tasks of controlling technological processes, finding the optimal control actions, and creating control algorithms that implement the optimal modes of technological processes, it is necessary to present the criterion of optimality in the form of a goal function, the extremum of which best meets the purpose of this object and expressed as - Relevant technical and economic indicators. The criterion of optimality should be an integral indicator that reflects the main aspects of production. Profit is most often taken as such a criterion for typical microbiological industries - as the most generalized indicator, reflecting almost all aspects of the enterprise. Possible criteria of optimality are analyzed in the form of technical and economic indicators of the process of cultivation of microorganisms, the extremum of which best meets the objectives of production and reflects the main aspects of the functioning of the control object. The analysis of possible modes of microalgae cultivation has been carried out. Two optimization algorithms are substantiated. The first one is based on random search method with an absolute bias, an algorithm for optimizing the process of cultivating microorganisms with continuous regeneration of the flow in one cultivator. The second is an algorithm for determining the optimal residence time of chlorella particles in multistage cultivators, focused on the method of dynamic programming implemented in Wellman's recurrence relation. The developed algorithm for operational forecasting and automatic control of the chlorella cultivation process allows, under given production conditions and the composition of nutrients, to increase the productivity of technological equipment and improve the quality of the target product, as well as to prevent in advance various unforeseen and emergency production situations.

A random search methodology for examining parametric uncertainty in water quality models

2006 ◽  
Vol 53 (1) ◽  
pp. 33-40 ◽  
Author(s):  
O.O. Osidele ◽  
W. Zeng ◽  
M.B. Beck
Keyword(s):  
Water Quality ◽  
High Speed ◽  
Nutrient Dynamics ◽  
Nearest Neighbor ◽  
Optimal Parameter ◽  
Random Search ◽  
Parametric Uncertainty ◽  
Simulation Models ◽  
Quality Model ◽  
Rejection Algorithm

The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values.

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