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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Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions
TL;DR: In this article , the authors compared the performance of sixteen meta-heuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) presented in the literature between 2021 and 2022.
Rules embedded harris hawks optimizer for large-scale optimization problems
TL;DR: In this paper , the authors developed embedded rules used to make adaptive switching between exploration/exploitation based on search performances, which significantly improved HHO in terms of accuracy and convergence curve.
Dynamic airport gate assignment with improved Shuffled Frog-Leaping Algorithm and triangle membership function
Hsien-Pin Hsu,Wan-Fang Yang,Tran Thi Bich Chau Vo +2 more
Dynamic Harris hawks optimizer based on historical information and tournament strategy and its application in numerical optimization of blast furnace ingredients
Zhendong Liu,Yiming Fang,Le Liu,Shuidong Ma +3 more
BCDDO: Binary Child Drawing Development Optimization
TL;DR: The suggested approach has significantly outperformed the previously discussed techniques in the area of feature selection to increase classification accuracy and is recommended for choosing the wrapper features in this study.
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![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)
