CPO: A Crow Particle Optimization Algorithm

Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local...
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Autor*in:	Huang, Ko-Wei [verfasserIn] Wu, Ze-Xue

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Metaheuristic algorithm Crow search algorithm Particle swarm optimization Function optimization Hybridization algorithm

Anmerkung:	© The Authors. Published by Atlantis Press SARL 2019

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 12(2018), 1 vom: Jan., Seite 426-435
Übergeordnetes Werk:	volume:12 ; year:2018 ; number:1 ; month:01 ; pages:426-435

Links:	Volltext

DOI / URN:	10.2991/ijcis.2018.125905658

Katalog-ID:	SPR054557194

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520			\|a Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms.
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allfields	10.2991/ijcis.2018.125905658 doi (DE-627)SPR054557194 (SPR)ijcis.2018.125905658-e DE-627 ger DE-627 rakwb eng Huang, Ko-Wei verfasserin aut CPO: A Crow Particle Optimization Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors. Published by Atlantis Press SARL 2019 Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213 Wu, Ze-Xue aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2018), 1 vom: Jan., Seite 426-435 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2018 number:1 month:01 pages:426-435 https://dx.doi.org/10.2991/ijcis.2018.125905658 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 01 426-435
spelling	10.2991/ijcis.2018.125905658 doi (DE-627)SPR054557194 (SPR)ijcis.2018.125905658-e DE-627 ger DE-627 rakwb eng Huang, Ko-Wei verfasserin aut CPO: A Crow Particle Optimization Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors. Published by Atlantis Press SARL 2019 Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213 Wu, Ze-Xue aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2018), 1 vom: Jan., Seite 426-435 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2018 number:1 month:01 pages:426-435 https://dx.doi.org/10.2991/ijcis.2018.125905658 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 01 426-435
allfields_unstemmed	10.2991/ijcis.2018.125905658 doi (DE-627)SPR054557194 (SPR)ijcis.2018.125905658-e DE-627 ger DE-627 rakwb eng Huang, Ko-Wei verfasserin aut CPO: A Crow Particle Optimization Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors. Published by Atlantis Press SARL 2019 Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213 Wu, Ze-Xue aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2018), 1 vom: Jan., Seite 426-435 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2018 number:1 month:01 pages:426-435 https://dx.doi.org/10.2991/ijcis.2018.125905658 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 01 426-435
allfieldsGer	10.2991/ijcis.2018.125905658 doi (DE-627)SPR054557194 (SPR)ijcis.2018.125905658-e DE-627 ger DE-627 rakwb eng Huang, Ko-Wei verfasserin aut CPO: A Crow Particle Optimization Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors. Published by Atlantis Press SARL 2019 Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213 Wu, Ze-Xue aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2018), 1 vom: Jan., Seite 426-435 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2018 number:1 month:01 pages:426-435 https://dx.doi.org/10.2991/ijcis.2018.125905658 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 01 426-435
allfieldsSound	10.2991/ijcis.2018.125905658 doi (DE-627)SPR054557194 (SPR)ijcis.2018.125905658-e DE-627 ger DE-627 rakwb eng Huang, Ko-Wei verfasserin aut CPO: A Crow Particle Optimization Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors. Published by Atlantis Press SARL 2019 Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213 Wu, Ze-Xue aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 12(2018), 1 vom: Jan., Seite 426-435 (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:12 year:2018 number:1 month:01 pages:426-435 https://dx.doi.org/10.2991/ijcis.2018.125905658 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018 1 01 426-435
language	English
source	Enthalten in International journal of computational intelligence systems 12(2018), 1 vom: Jan., Seite 426-435 volume:12 year:2018 number:1 month:01 pages:426-435
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topic_facet	Metaheuristic algorithm Crow search algorithm Particle swarm optimization Function optimization Hybridization algorithm
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author	Huang, Ko-Wei
spellingShingle	Huang, Ko-Wei misc Metaheuristic algorithm misc Crow search algorithm misc Particle swarm optimization misc Function optimization misc Hybridization algorithm CPO: A Crow Particle Optimization Algorithm
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topic_title	CPO: A Crow Particle Optimization Algorithm Metaheuristic algorithm (dpeaa)DE-He213 Crow search algorithm (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Hybridization algorithm (dpeaa)DE-He213
topic	misc Metaheuristic algorithm misc Crow search algorithm misc Particle swarm optimization misc Function optimization misc Hybridization algorithm
topic_unstemmed	misc Metaheuristic algorithm misc Crow search algorithm misc Particle swarm optimization misc Function optimization misc Hybridization algorithm
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title	CPO: A Crow Particle Optimization Algorithm
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title_full	CPO: A Crow Particle Optimization Algorithm
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title_sort	cpo: a crow particle optimization algorithm
title_auth	CPO: A Crow Particle Optimization Algorithm
abstract	Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. © The Authors. Published by Atlantis Press SARL 2019
abstractGer	Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. © The Authors. Published by Atlantis Press SARL 2019
abstract_unstemmed	Abstract Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. © The Authors. Published by Atlantis Press SARL 2019
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title_short	CPO: A Crow Particle Optimization Algorithm
url	https://dx.doi.org/10.2991/ijcis.2018.125905658
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score	7.401021

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CPO: A Crow Particle Optimization Algorithm

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