Child Drawing Development Optimization Algorithm based on Child’s Cognitive Development
TL;DR: A novel metaheuristic Child Drawing Development Optimization algorithm inspired by the child's learning behaviour and cognitive development using the golden ratio to optimize the beauty behind their art and reveals the competency of the algorithm to evade local minima.
read more
Abstract: This paper proposes a novel metaheuristic Child Drawing Development Optimization (CDDO) algorithm inspired by the child's learning behavior and cognitive development using the golden ratio to optimize the beauty behind their art. The golden ratio was first introduced by the famous mathematician Fibonacci. The ratio of two consecutive numbers in the Fibonacci sequence is similar, and it is called the golden ratio, which is prevalent in nature, art, architecture, and design. CDDO uses golden ratio and mimics cognitive learning and child's drawing development stages starting from the scribbling stage to the advanced pattern-based stage. Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results. This helps children with evolving, improving their intelligence and collectively achieving shared goals. CDDO shows superior performance in finding the global optimum solution for the optimization problems tested by 19 benchmark functions. Its results are evaluated against more than one state-of-art algorithms such as PSO, DE, WOA, GSA, and FEP. The performance of the CDDO is assessed, and the test result shows that CDDO is relatively competitive through scoring 2.8 ranks. This displays that the CDDO is outstandingly robust in exploring a new solution. Also, it reveals the competency of the algorithm to evade local minima as it covers promising regions extensively within the design space and exploits the best solution.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Figures
Citations
An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges
Kusum Deep,Swagatam Das +1 more
TL;DR: More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade as mentioned in this paper , and approximately 540 MAs are tracked, and statistical information is also provided.
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review
TL;DR: In this article , a review of NIOA-based multi-level thresholding models is presented, highlighting and exploring the major challenges encountered during the development of image multi-thresholding models.
Meta-heuristic search algorithms in truss optimization: Research on stability and complexity analyses
Hasan Öztürk,H. Tolga Kahraman +1 more
TL;DR: In this article , the authors designed a simulation environment with defined standards and a benchmarking suite consisting of nine structural truss bar problems (TPs) of three different types in order to eliminate these problems.
30
Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)
Ali Mohammadi,Farid Sheikholeslam +1 more
TL;DR: This literature review (1965-2022) surveys 320+ nature-inspired optimization algorithms, categorizing them into evolution-based, swarm-based, physics-based, human-based, and hybrid-based methods, with statistical analysis of publications and top-ranked publishers.
29
An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization
Fatma A. Hashim,Essam H. Houssein,Reham R. Mostafa,Abdelazim G. Hussien,Fatma Helmy +4 more
TL;DR: This study proposes an adaptive-mutated Coati optimization algorithm (mCoatiOA) for feature selection and global optimization, outperforming 8 competitive algorithms on 8 test functions and achieving better results on 75% of 15 benchmark datasets.
23
References
Swarm Intelligence Algorithms for Data Clustering
Ajith Abraham,Swagatam Das,Sandip Roy +2 more
- 01 Jan 2008
TL;DR: A new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization is described, which may come with a variety of attributes or features.
Geometrical Substantiation of Phi , the Golden Ratio and the Baroque of Nature, Architecture, Design and Engineering
TL;DR: In this paper, a panoptic view of the miraculous Golden Proportion and its relation with the nature, globe, universe, arts, design, mathematics and science is presented in this study.
Nature-Inspired Algorithms: State-of-Art, Problems and Prospects
Parul Agarwal,Shikha Mehta +1 more
TL;DR: This study provides the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of required input parameters, their key evolutionary strategies and application areas to overcome the problem of ‘curse of dimensionality’.
Donkey and smuggler optimization algorithm: A collaborative working approach to path finding
Ahmed S. Shamsaldin,Tarik A. Rashid,Rawan A. Al-Rashid Agha,Nawzad K. Al-Salihi,Mokhtar Mohammadi +4 more
TL;DR: In this paper, a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO) is proposed, which is inspired by the searching behavior of donkeys in the real world.
Nature-Inspired Mateheuristic Algorithms: Success and New Challenges
TL;DR: The most important part of optimization is the core algorithms used to find optimal solutions to a given problem, though in many cases such algorithms may not exist at all.

![Figure 3:Random scribble [32]](/figures/figure-3-random-scribble-32-2bs16wrn.png)

![Figure 4: Controlled scribbles [32]](/figures/figure-4-controlled-scribbles-32-3og4bvvo.png)
![Figure 5: Head and Feet Symbols [32]](/figures/figure-5-head-and-feet-symbols-32-ur2otubo.png)
