Investigation of cricket behaviours as evolutionary computation for system design optimization problems

Canayaz, Murat; Karcı, Ali

doi:10.1016/j.measurement.2015.02.052

Cited by 10 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 2022 ) 141 Covariance Matrix Adaptation-Evolution Strategy (CMAES) Hansen et al. ( 2003 ) 142 Coyote Optimization Algorithm (COA) Pierezan and Coelho ( 2018 ) 143 Cricket Algorithm (CA) Canayaz and Karcı ( 2015 ) 144 Cricket Behaviour-Based Algorithm (CBA) Canayaz and Karci ( 2016 ) 145 Cricket Chirping Algorithm (CCA) Deuri and Sathya ( 2018 ) 146 Crow Search Algorithm (CSA) Askarzadeh ( 2016 ) 147 Crystal Energy Optimization Algorithm (CEO) Feng et al. ( 2016 ) 148 Crystal Structure Algorithm (CryStAl) Talatahari et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

View full text Add to dashboard Cite

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

show abstract

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

View full text Add to dashboard Cite

show abstract

“…In this study, we design an artificial bee colony (ABC) algorithm to solve the FSP in the same network using a recently proposed distance function for comparison of fuzzy numbers. The use of evolutionary computation techniques and algorithms such as the ABC is increasing in different measurement applications, due to their capacity to operate within complex environments and provide accurate solutions to the optimization problem being considered (Canayaz and Karcı, 2015;Özdağ and Karcı, 2016;Singh and Chatterjee, 2013). In this regard, it will be shown that the proposed method significantly reduces the complexities encountered in the existing methods.…”

Section: Introductionmentioning

confidence: 99%

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

2016

View full text Add to dashboard Cite

“…Because of the fact that the developed approach is swarm-based, the structure of the algorithm was designed to be similar to those behaviors of foraging swarms such as PSO [10]. Additionally, the best aspects of Bat [11] and Firefly [12,20] algorithms which were developed by Yang were taken and applied on the algorithm during the construction stage of the algorithm that was initially presented and published in [24][25][26]. Considering the algorithms developed by Yang, the utilized properties of bats in bat algorithm is that bats use their tonal attributes against obstacles and their hunts; in firefly algorithm fireflies head towards the fireflies with highest intensity light and thanks to this feature they do mate.…”

Section: Introductionmentioning

confidence: 99%

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Canayaz

Karcı

2015

Appl Intell

Self Cite

View full text Add to dashboard Cite

Meta-heuristic algorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired metaheuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

show abstract

Investigation of cricket behaviours as evolutionary computation for system design optimization problems

Cited by 10 publications

References 25 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

Contact Info

Product

Resources

About