Wolf search algorithm with ephemeral memory

Tang, Rui; Fong, Simon; Yang, Xin-She; Deb, Suash

doi:10.1109/icdim.2012.6360147

Cited by 167 publications

(75 citation statements)

References 9 publications

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“…Beberapa penelitian SI menekankan pada spesifik perilaku hewan yang menjadi sumber inspirasinya, yaitu antara lain algoritma rayap untuk menyelesaikan masalah clustering [3], algoritma pencarian serigala [4], algoritma optimalisasi kucing [5], strategi berburu elang [6], algoritma cuckoo search dan kunang-kunang [7], algoritma bacterial foraging [8], dan optimasi koloni rayap [9]. Penelitian tersebut perlu diuji untuk membuktikan apakah algoritma dalam rumpun SI tersebut merupakan bagian dari rumpun ilmu CAS atau tidak memiliki karakteristik sesuai dengan teori CAS walaupun dasar dari inspirasinya berasal dari interaksi kolektif mahluk hidup.…”

Section: Pendahuluanunclassified

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Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Bintoro

Permana

2017

J. Teknol. dan Sist. Komput.

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- [9]. Penelitian tersebut perlu diuji untuk membuktikan apakah algoritma dalam rumpun SI tersebut merupakan bagian dari rumpun ilmu CAS atau tidak memiliki karakteristik sesuai dengan teori CAS walaupun dasar dari inspirasinya berasal dari interaksi kolektif mahluk hidup.Klasifikasi SI sebagian besar dilakukan untuk memetakan SI berdasarkan mekanisme biomimetiknya [10]. Xiao dkk [11] melakukan pendekatan analisis kemiripan antara SI dan jaringan syaraf tiruan (JST). Penelitian ini bertujuan untuk mengklasifikasikan SI berdasarkan kesamaan karakteristiknya terhadap CAS. menempatkan algoritma SI tersebut menjadi objek yang akan diklasifikasikan dilihat dari sudut pandang CAS. Teknik uji komparasi digunakan sebagai pendekatan pembuktian. Uji komparasi algoritma akan digunakan untuk mengelompokkan beberapa algoritma dalam SI yang masuk ke dalam CAS dan algoritma mana saja yang tidak memiliki karakteristik CAS.

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Section: Pendahuluanunclassified

“…Jumlah entitas pada ketiga algoritma dalam kelompok ini, yaitu [4], [16], dan [21], terdiri dari lebih dari satu entitas (E). Perbedaan mendasar terlihat pada pola komunikasi di antara ketiga algoritma tersebut.…”

Section: Metode Penelitianunclassified

Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Bintoro

Permana

2017

J. Teknol. dan Sist. Komput.

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“…Along with GSA, other evolutionary and swarm-based metaheuristic algorithms, including Genetic Algorithm (GA), Evolutionary Programming (EP), Evolution Strategies (ES), and PSO, were also tested on the same functions for comparisons based on mean and standard deviations of best values found over designated runs. Soliman and Rassem in [10] and Tang et al in [11] both employed 8 test functions comprising of unimodal and multimodal categories with 30 dimensions. But, the number of runs in both the experiments was different, 30 in [10] and 100 in [11].…”

Section: Benchmark Functions Used In Literaturementioning

confidence: 99%

“…Soliman and Rassem in [10] and Tang et al in [11] both employed 8 test functions comprising of unimodal and multimodal categories with 30 dimensions. But, the number of runs in both the experiments was different, 30 in [10] and 100 in [11]. The prior work introduced Estimation of Distribution Algorithm (EDA), while Wolf Search Algorithm (WSA) was the proposed method in later research.…”

Section: Benchmark Functions Used In Literaturementioning

confidence: 99%

Common Benchmark Functions for Metaheuristic Evaluation: A Review

Hussain

Salleh

Cheng

et al. 2017

JOIV : Int. J. Inform. Visualization

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Abstract-In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. Algorithms that perform well on a set of numerical optimization problems are considered as effective methods for solving real-world problems. Different researchers choose different set of functions with varying configurations, as there exists no standard or universally agreed test-bed. This makes hard for researchers to select functions that can truly gauge the robustness of a metaheuristic algorithm which is being proposed. This review paper is an attempt to provide researchers with commonly used experimental settings, including selection of test functions with different modalities, dimensions, the number of experimental runs, and evaluation criteria. Hence, the proposed list of functions, based on existing literature, can be handily employed as an effective test-bed for evaluating either a new or modified variant of any existing metaheuristic algorithm. For embedding more complexity in the problems, these functions can be shifted or rotated for enhanced robustness.

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“…It is one of the recent meta heuristic algorithm by Simon fong (85). Wolf algorithm is based on the behavior of wolfs for hunting.…”

Section: A Basic Overviewmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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Wolf search algorithm with ephemeral memory

Cited by 167 publications

References 9 publications

Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik

Common Benchmark Functions for Metaheuristic Evaluation: A Review

A Review of Bio Inspired Computing and its Applications

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