Int. J. Advanced Networking and Applications Volume: 14 Issue: 06 Pages: 5696 - 5704 (2023) ISSN: 0975-0290 5696 CIDO: Chaotically Initialized Dandelion Optimization for Global Optimization Sinem Akyol Department of Software Engineering, Firat University, Elazig-23000 Email: sakyol@firat.com Muhammed Yildirim Department of Computer Engineering, Malatya TurgutOzal University, Malatya-44000 Email:muhammed.yildirim@ozal.edu.tr Bilal Alatas Department of Software Engineering, Firat University, Elazig-23000 Email: balatas@firat.com -------------------------------------------------------------------ABSTRACT-------------------------------------------------------------- Metaheuristic algorithms are widely used for problems in many fields such as security, health, engineering. No metaheuristic algorithm can achieve the optimum solution for all optimization problems. For this, new metaheuristic methods are constantly being proposed and existing ones are being developed. Dandelion Optimizer, one of the most recent metaheuristic algorithms, is biology-based. Inspired by the wind-dependent long-distance flight of the ripening seed of the dandelion plant. It consists of three phases: ascending phase, descending phase and landing phase. In this study, the chaos-based version of Chaotically Initialized Dandelion Optimizer is proposed for the first time in order to prevent Dandelion Optimizer from getting stuck in local solutions and to increase its success in global search. In this way, it is aimed to increase global convergence and to prevent sticking to a local solution. While creating the initial population of the algorithm, six different Chaotically Initialized Dandelion Optimizer algorithms were presented using the Circle, Singer, Chebyshev, Gauss/Mouse, Iterative and Logistic chaotic maps. Two unimodal (Sphere and Schwefel 2.22), two multimodal (Schwefel and Rastrigin) and two fixed-dimension multimodal (Foxholes and Kowalik) quality test functions were used to compare the performances of the algorithms. When the experimental results were analyzed, it was seen that the Chaotically Initialized Dandelion Optimizer algorithms gave successful results compared to the classical Dandelion Optimizer. Keywords -Metaheuristic Algorithms, Dandelion Optimizer, Chaos, Global Optimization ----------------------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: October 28, 2022 Date of Acceptance: April 10, 2023 ----------------------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION The aim of optimization is to search for the best between all solution candidates for the interested problem under specific constraints. Although the solution methodologies in classical optimization algorithms mostly depend on the variable types (real, integer, etc.), objective and constraint functions (non-linear, linear, etc.) used in the problem modeling, their effectiveness also depend on the solution method. Also, classical optimization algorithms do not provide applicable general solution strategies for problem formulations that consist of different types of decision variables, constraint functions, and objectives functions. Therefore, metaheuristic optimization algorithms are suggested. These techniques have gained considerable popularity due to their high computational efficiency and simplicity in their transformation. General purposed metaheuristic algorithms can be categorized into eight groups: swarm-based, biology-based, physics-based, chemistry-based, social-based, music-based, mathematics- based, and sports-based [1][2]. Hybridization of these algorithms can also be categorized as a different group [3]. A perennial herb belonging to the Asteraceae family, the dandelion is officially known as Herbataraxaci. These plants have heads that can grow to be more than 20 cm tall, and they resemble inflorescences [4]. The wind transfers their mature seeds to new locations where they will nourish life. The crown hairs, which are crucial for the dissemination of the seeds, delay their landing so that the wind can carry them farther. The most emblematic plant that depends on the wind for seed dispersion is the dandelion. Under the correct circumstances, its seed can travel tens of kilometers in the wind [5]. The two main parameters that influence the dispersal of dandelion seeds are wind velocity and climate. When determining whether a seed is traveling over long or short distances, wind speed is used [6]. Weather affects the ability of dandelions to grow near or far by controlling whether dandelion seeds can fly. Dandelion seeds go through three stages. In the first stage, the ascent stage, a vortex forms on the dandelion seed and rises under the influence of drag in sunny and windy weather. In contrast, there is no vortex on the seeds when it is raining. In this situation, only local searches are possible. In the second stage, the descending stage, the seeds fall continuously after reaching a certain height. Dandelions eventually reproduce through seed, which at this stage lands randomly under the influence of wind and weather. Dandelion improves its populations by passing its seeds through these three stages to the next generation. Dandelion Optimization, one of the most recent metaheuristic algorithms, has been developed inspired by these three stages [7]. In this study, chaotic version of Dandelion Optimizer is proposed for the first time in order to increase its performance. CIDO algorithms were developed using the initial population chaotic maps. Circle, Singer, Chebyshev, Gauss/Mouse, Iterative and Logistic maps were used as chaotic maps. In the second part, classical DO is introduced