# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 4 Experimental Procedure
### Evaluated Optimization Algorithms  
The optimization algorithms evaluated in this study encompass both contemporary and classical methods, offering a comprehensive comparison across a wide range of approaches. The synergistic swarm optimization algorithm (SSOA), introduced by [PERSON] in 2024, represents the latest advancements in swarm-based optimization techniques [40]. Similarly, the frilled lizard optimization (FLO) algorithm, developed by [PERSON] in 2024, reflects innovative developments in biologically inspired optimization methods [41]. The Chinese pangolin optimizer (CPO), designed by [PERSON] in 2024, further exemplifies the ongoing evolution of animal-inspired algorithms [42]. Additionally, the four-vector optimizer (FVIM), also introduced in 2024, utilizes vector-based approaches to tackle optimization problems [43].  
Among the slightly older algorithms, the success history intelligent optimizer (SHIO), proposed by [PERSON] in 2022, incorporates historical success data to enhance optimization performance [44]. The zebra optimization algorithm (ZOA), designed by [PERSON] in 2022, and the dingo optimization algorithm (DOA), introduced by [PERSON] in 2021, continue the exploration of nature-inspired optimization strategies [45; 46]. The renorm optimization algorithm (ROA) and aquila optimizer (AO), both from 2021, contribute to the diversity of bio-inspired optimization techniques [47; 48]. The chimp optimization algorithm (CHIMP), introduced in 2020, adds another animal-inspired methodology to this growing collection of techniques [49].  
Earlier contributions to the field include the sooty term optimization algorithm (STOA), developed in 2019, and the seagull optimization algorithm (SOA), also introduced in 2019, both of which draw inspiration from avian behaviors [50; 51]. The SCA optimization algorithm (SCSO), introduced in 2023, further demonstrates the continuous innovation in algorithms inspired by animal behavior [52].  
Classical algorithms that have demonstrated enduring relevance include the multi-verse optimizer (MVO), the whale optimization algorithm (WOA), and the sine cosine algorithm (SCA), all introduced by [PERSON] in 2016 [53; 54; 55]. The moth-flame optimization algorithm (MFO), introduced in 2015, and the grey wolf optimizer (GWO), proposed in 2014, also by [PERSON], continue to be widely utilized due to their robust performance across a variety of optimization problems [56; 57].  
Older, yet foundational, methods include the particle swarm optimization (PSO) algorithm, developed in 1995, which remains one of the most influential swarm intelligence algorithms [58]. The simulated annealing algorithm (SA), introduced in 1983, and the genetic algorithm (GA), developed in the 1960s, represent early yet enduring contributions to the field of optimization [59; 60]. These classical algorithms continue to serve as benchmarks for evaluating newer techniques.  
This selection of algorithms, ranging from the most recent innovations to well-established methodologies, provides a robust framework for evaluating performance across a diverse array of optimization challenges.