Chaotic Rough Particle Swarm Optimization Algorithms

Alataş, Bilal; Akın, Erhan

doi:10.5772/5094

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Metaheuristic algorithms can be nature inspired or based on social behavior or musical. In addition, there are hybrid algorithms that are improved by utilizing some heuristic algorithms [12]. But the goal of all methods is to achieve acceptable solutions using existing knowledge properly.…”

Section: Introductionmentioning

confidence: 99%

An Improved Tree Seed Algorithm for Optimization Problems

ASLAN¹,

Beşkirli²,

Kodaz³

et al. 2018

IJMLC

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Various heuristic algorithms have been proposed in the literature for solving optimization problems. Tree-seed algorithm (TSA) is inspired from relation between trees and seeds that a population-based evolutionary algorithm. when create process of a seed occur in TSA, the position updating of each dimension of the seed is calculated separately. In scope this study, Some changes have been implemented to original TSA. A new operator was added to the position update equation of original TSA when create a seed from tree. This operator is calculated by dynamically according to the dimension of the problem. As the dimension of the problem increases, the value of the this operator decreases. In addition, we determined an upper and a lower bound for the update process of the seed. The Improved Tree Seed Algorithm (ITSA) proposed in this study and the TSA have been tested on some benchmark functions in the literature. As a result, when the experimental results are taken into consideration, it is understood that the proposed algorithm ITSA is obtained more effective results for benchmark functions than TSA. Moreover, it is observed that ITSA found quite successful results compared with TSA for large-scale benchmark problems.

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Section: Introductionmentioning

confidence: 99%

An Improved Tree Seed Algorithm for Optimization Problems

ASLAN¹,

Beşkirli²,

Kodaz³

et al. 2018

IJMLC

View full text Add to dashboard Cite

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“…Hence metaheuristic algorithms solve optimization problems via high level techniques [3]- [5]. Heuristic Manuscript received July 10, 2013; accepted November 29, 2013. methods are divided into six different groups as biologicalbased, physical-based, social-based, swarm-based, musicalbased and chemical-based [6], [7]. Genetic Algorithm [8] and Differential Evolution Algorithm [9] are grouped as biology-based algorithm; Gravitational Search Algorithm [10] and Electromagnetism Algorithm [11] are grouped as physics-based algorithm; Tabu Search [12] is grouped as social-based algorithm; Particle Swarm Optimization [13], Ant Colony Optimization [14] are grouped as swarm-based algorithm; Harmony Search Algorithm [3] is grouped as music-based algorithm; Artificial Chemical Reaction Algorithm [15] is grouped as chemical-based algorithm.…”

Section: Introductionmentioning

confidence: 99%

Modified Bat Algorithm

Yılmaz

Küçüksille

Cengiz

2014

ElAEE

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Heuristic optimization algorithms which are inspired by nature have become very popular for solving real world problems recently. The use of these algorithms increases day by day in the literature because of their flexible structures and non-containing confusing mathematical terms. One of these algorithms is Bat Algorithm (BA). BA is a heuristic algorithm based on echolocation characteristic of bats and developed by the mimics of bats' foraging behaviour. In this study exploration mechanism of the algorithm is improved by modifying the equation of pulse emission rate and loudness of bats. The performance of Modified Bat Algorithm (MBA) is verified by 15 benchmark functions and the results were exhibited as comparative. The results of MBA are superior in terms of solution quality on optimization problems compared to BA.

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Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos-enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos-based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems.The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods.According to the obtained results, the proposed method seems promising with respect to different metrics.

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Chaotic Rough Particle Swarm Optimization Algorithms

Cited by 3 publications

References 15 publications

An Improved Tree Seed Algorithm for Optimization Problems

An Improved Tree Seed Algorithm for Optimization Problems

Modified Bat Algorithm

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

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