Tomas Kadavy

Hypersphere Universe Boundary Method Comparison on HCLPSO and PSO

In this paper, the hypersphere universe method is applied on Heterogeneous Comprehensive Learning Particle Swarm Optimization (HCLPSO) and a classical representative of swarm intelligence Particle Swarm Optimization (PSO). The goal is to the compare this method to the classical version of these algorithms. The comparisons are made on CEC’17 benchmark set functions. The experiments were carried out according to CEC benchmark rules and statistically evaluated using Friedman rank test.

A Review of Real-World Applications of Particle Swarm Optimization Algorithm

In this work, we present an overview of the various real-world application of Particle Swarm Optimization Algorithm. We argue that the PSO is showing superior performance on different optimization problems such as temperature prediction, battery storage optimization or leukemia diagnosis. The diversity of real-world applications covers the fields of electronic, informatics, energetics, medicine and many other areas of industry and research. This study should encourage new researchers for applying this method and take advantage of its unique inner dynamic and performance.

Differential Evolution Driven Analytic Programming for Prediction

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of connection between AP and different strategies of DE. AP can be considered as powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

Archive Analysis in SHADE

The aim of this research paper is to analyze the current optional archive in Success-History based Adaptive Differential Evolution (SHADE) which is used during mutation. The usefulness of the archive is analyzed on CEC 2015 benchmark set of test functions where the impact of successful archive use on final test function value is studied. This paper also proposes a new version of optional archive named Enhanced Archive (EA), which is also tested on CEC 2015 benchmark set and the results are compared with the canonical version. Two research questions are discussed: Whether SHADE with EA has better performance than canonical SHADE and whether it makes a better use of the archive.

Performance comparison of differential evolution driving analytic programming for regression

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. This paper provides a closer insight into performance comparisons of connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategies, which is Success-History based Adaptive Differential Evolution (SHADE) and its variant L-SHADE. Simple experiments have been carried out here with the four different time series of EUR/USD exchange rate. DE performance analysis, as well as the differences between regression models synthesized using AP as direct consequences of different DE strategies performances, are both discussed within conclusion section.

PSO with Partial Population Restart Based on Complex Network Analysis

This study presents a hybridization of Particle Swarm Optimization with a complex network creation and analysis. A partial population is performed in certain moments of the run of the algorithm based on the information obtained from a complex network structure that represents the communication in the population. We present initial results alongside statistical evaluation and discuss future possibilities of this approach.

On the Randomization of Indices Selection for Differential Evolution

This research deals with the hybridization of two softcomputing fields, which are the chaos theory and evolutionary algorithms. This paper investigates the utilization of the two-dimensional discrete chaotic systems, which are Burgers and Lozi maps, as the chaotic pseudo random number generators (CPRNGs) embedded into the selected heuristics, which is differential evolution algorithm (DE). Through the utilization of either chaotic systems or identical identified pseudo random number distribution, it is possible to fully keep or remove the hidden complex chaotic dynamics from the generated pseudo random data series. Experiments are focused on the extended investigation, whether the different randomization types with different pseudo random numbers distribution or hidden complex chaotic dynamics providing the unique sequencing are more beneficial to the heuristic performance. This research utilizes set of 4 selected benchmark functions, and totally four different randomizations; further results are compared against canonical DE.

Comparing Border Strategies for Roaming Particles on Single and Multi-swarm PSO

In this paper, the methods for handling particles that violate available search spaces are compared using a single and multi-swarm technique. The methods are soft borders and hypersphere universe. The goal is to compare this approaches and its combination. The comparisons are made on CEC’17 benchmark set functions. The experiments were carried out according to CEC benchmark rules and statistically evaluated.

Differential Evolution and Chaotic Series

This research deals with the modern and popular hybridization of chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10

Addressing Premature Convergence with Distance based Parameter Adaptation in SHADE

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