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.
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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.
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References
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
15K
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
11.1K
A New Heuristic Optimization Algorithm: Harmony Search
Zong Woo Geem,Joong Hoon Kim,G. V. Loganathan +2 more
- 01 Feb 2001
TL;DR: A new heuristic algorithm, mimicking the improvisation of music players, has been developed and named Harmony Search (HS), which is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
6.3K
•Book
Nature-Inspired Metaheuristic Algorithms
Xin-She Yang
- 01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
4.9K

![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)
