Ideology algorithm: a socio-inspired optimization methodology

Huan, Teo Ting; Kulkarni, Anand J.; Kanesan, Jeevan; Chuah, Joon Huang; Abraham, Ajith

doi:10.1007/s00521-016-2379-4

Cited by 71 publications

(22 citation statements)

References 49 publications

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“…The industry and academic community are tremendously paying attention to this field of knowledge [5]. Being a global method, metaheuristic methods trying to stimulate natural phenomena (particle swarm optimization [6], genetic algorithm [7], ant lion optimizer (ALO) [8], Cyclical Parthenogenesis Algorithm (CPA) [9]), socio-cultural behaviors (socio evolution and learning optimization (SELO) [10] and Ideology Algorithm (IA) [11]), or physical phenomena (colliding bodies optimization [5], gravitational search algorithm (GSA) [12], charged system search (CSS) [13]). Metaheuristic optimization methods have two unique, distinctive aspects: exploration and exploitation.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

An Upgraded Sine Cosine Algorithm for Tower Crane Selection and Layout Problem

Kaveh¹,

Vazirinia²

2020

Period. Polytech. Civil Eng.

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Tower crane is the core construction facility in the high-rise building construction sites. Proper selection and location of construction tower cranes not only can affect the expenses but also it can have impact on the material handling process of building construction. Tower crane selection and layout problem (TCSLP) is a type of construction site layout problem, which is considered as an NP-hard problem. In consequence, researchers have extensively used metaheuristics for their solution. The Sine Cosine Algorithm (SCA) is a newly developed metaheuristic which performs well for TCSLP, however, efficient use of this algorithm requires additional considerations. For this purpose, the present paper studies an upgraded sine cosine algorithm (USCA) that employs a harmony search based operator to improve the exploration and deal with variable constraints simultaneously and uses an archive to save the best solutions. Subsequently, the upgraded sine cosine algorithm is employed to optimize the locations to find the best tower crane layout. Several benchmark functions are studied to evaluate the performance of the USCA. A comparative study indicates that the USCA performs quite well in comparison to other recently developed metaheuristic algorithms.

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Section: Metaheuristic Algorithmsmentioning

confidence: 99%

An Upgraded Sine Cosine Algorithm for Tower Crane Selection and Layout Problem

Kaveh¹,

Vazirinia²

2020

Period. Polytech. Civil Eng.

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“…It imparts exploration as well as exploitation abilities to entire swarm. According to Teo et al (2016), Li and Yao (2012) and Selvi and Umrani (2010) the PSO may not be efficient solving the problems with discrete search space as well as non-coordinate systems and may need supporting techniques to solve such problems. In this paper Multi-CI is compared with the advanced versions of the PSO referred to as Comprehensive Learning PSO (CLPSO) (Liang et al, 2016) and PSO2011 (Omran and Clerc2011).The technique of CMAES (Igel et al 2007) is a mathematical-based algorithm which exploits adaptive mutation parameters through computing a covariance matrix.…”

Section: Learning Attemptsmentioning

confidence: 99%

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

Shastri

Kulkarni

2018

International Journal of Parallel, Emergent and Distributed Sys

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A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimization domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimization, Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-adaptive differential evolution algorithm, Comprehensive Learning Particle Swarm Optimization, Backtracking Search Optimization Algorithm and Ideology Algorithm. The Wilcoxon signed rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations are discussed as well. In addition, an illustrative example is also solved and every detail is provided.

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“…Human-based algorithms class contains an algorithm that simulates human beings’ behavior. Examples of Human-based algorithms are teaching-learning based optimization (TLBO) algorithm [ 20 ], socio evolution and learning optimization (SELO) algorithm [ 21 ], cognitive behavior optimization algorithm (COA) [ 22 ], the ideology algorithm [ 23 ], coronavirus herd immunity optimizer (CHIO) [ 24 ], human mental search (HMS) [ 25 ], and social learning optimization (SLO) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Lightning search algorithm: a comprehensive survey

et al. 2020

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The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper.

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Ideology algorithm: a socio-inspired optimization methodology

Cited by 71 publications

References 49 publications

An Upgraded Sine Cosine Algorithm for Tower Crane Selection and Layout Problem

An Upgraded Sine Cosine Algorithm for Tower Crane Selection and Layout Problem

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

Lightning search algorithm: a comprehensive survey

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