Solving multiobjective problems using cat swarm optimization

Pradhan, Pyari Mohan; Panda, Ganapati

doi:10.1016/j.eswa.2011.08.157

Cited by 170 publications

(64 citation statements)

References 9 publications

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“…Recently, in order to deal with multi-criteria optimization problems, Pradhan and Panda (2012) developed a new multiobjective cat swarm optimization (called MOCSO), in which the concept of external archive and Pareto dominance is incorporated. The basic idea of the Multiobjective CSO (MOCSO) algorithm utilized the major structure of the CSO method.…”

Section: Multiobjective Cso Algorithmmentioning

confidence: 99%

“…To quantify the efficient of MOCSO, some performance metrics, namely, set coverage metric, generational distance, maximum Pareto-optimal front error, Spacing, and Spread are tested in (Pradhan and Panda 2012). In addition, two multiobjective tested function are proposed.…”

Section: Performance Of Mocsomentioning

confidence: 99%

“…• Overall, the procedures of the MOCSO algorithm are summarized as follows (Pradhan and Panda 2012): • Step 1: Randomly initialize the position of cats in D-dimensional space, i.e., X id representing position of ith cat in dth dimension.…”

Section: Tracing Mode Of Mocsomentioning

confidence: 99%

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Cat Swarm Optimization Algorithm

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, we present a new population-based method, called cat swarm optimization (CSO) algorithm, which imitates the natural behaviour of cats. We first describe the general knowledge of the behaviour of cats in Sect. 6.1. Then, the fundamentals and performance of CSO are introduced in Sect. 6.2. Next, some selected variations of CSO are explained in Sect. 6.3. Right after this, Sect. 6.4 presents a representative CSO application. Finally, Sect. 6.5 summarises this chapter. IntroductionCats exhibit fascinated social behaviours that have long since attracted the attention of human beings. When we were young, we may have observed that cats have a strong curiosity towards moving objects. We may have also discovered that even though cats spend most of their time in resting, they always remain alert and can possess a good hunting skill. Inspired by these behavioural pattern, Chu and Tsai (2007) proposed a new optimization algorithm called cat swarm optimization (CSO) that involves two modes (i.e., seeking and tracing) of operations for solving complex optimization problems. Behaviour of CatsNowadays, one of the most popular companion animals can be found in homes throughout the world is the domestic cats. However, cat social behaviour is complex and incompletely understood. Recently, researchers have focused their attention on two categories, i.e., resting behaviour and chasing behaviour.

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Section: Multiobjective Cso Algorithmmentioning

confidence: 99%

Section: Performance Of Mocsomentioning

confidence: 99%

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Cat Swarm Optimization Algorithm

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…tarafından önerilmiştir [20]. Pradhan ve Panda, çok amaçlı optimizasyon problemlerini çözmek için, mevcut KSO algoritmasını genişleterek yeni çok amaçlı bir evrimsel algoritma önermişlerdir [21]. Wang vd.…”

Section: Kedi̇ Sürüsü Opti̇mi̇zasyonu (Cat Swarm Optimization)unclassified

Kedi̇ Sürüsü Opti̇mi̇zasyon Algori̇tmasiyla Doğru Ve Anlaşilabi̇li̇r Nümeri̇k Siniflandirma Kurallarinin Otomati̇k Keşfi̇

Alataş

Akyol

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…Hence, how to design an efficient data forwarding path and to maximally extend the life cycle of the WSN become the principal issues. Answering to these needs, enhanced parallel cat swarm optimization (EPCSO) [2][3][4][5] is modified partially and is employed to produce the balanced paths for the whole WSN. This is the first application that utilizes EPCSO in the routing design for WSN.…”

Section: Introductionmentioning

confidence: 99%

A Balanced Power Consumption Algorithm Based on Enhanced Parallel Cat Swarm Optimization for Wireless Sensor Network

Kong

Pan

Tsai

et al. 2015

International Journal of Distributed Sensor Networks

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The wireless sensor network (WSN) is composed of a set of sensor nodes. It is deemed suitable for deploying with large-scale in the environment for variety of applications. Recent advances in WSN have led to many new protocols specifically for reducing the power consumption of sensor nodes. A new scheme for predetermining the optimized routing path is proposed based on the enhanced parallel cat swarm optimization (EPCSO) in this paper. This is the first leading precedent that the EPCSO is employed to provide the routing scheme for the WSN. The experimental result indicates that the EPCSO is capable of generating a set of the predetermined paths and of smelting the balanced path for every sensor node to forward the interested packages. In addition, a scheme for deploying the sensor nodes based on their payload and the distance to the sink node is presented to extend the life cycle of the WSN. A simulation is given and the results obtained by the EPCSO are compared with the AODV, the LD method based on ACO, and the LD method based on CSO. The simulation results indicate that our proposed method reduces more than 35% power consumption on average.

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Solving multiobjective problems using cat swarm optimization

Cited by 170 publications

References 9 publications

Cat Swarm Optimization Algorithm

Cat Swarm Optimization Algorithm

Kedi̇ Sürüsü Opti̇mi̇zasyon Algori̇tmasiyla Doğru Ve Anlaşilabi̇li̇r Nümeri̇k Siniflandirma Kurallarinin Otomati̇k Keşfi̇

A Balanced Power Consumption Algorithm Based on Enhanced Parallel Cat Swarm Optimization for Wireless Sensor Network

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