Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization

Adaptive division of labor particle swarm optimization

“…The theoretical analysis discards hardware factors, such as operating systems, programming languages, and compilers used to execute the tested algorithm during the complexity evaluation process. In most cases, the theoretical analysis reports the worst-case complexity although average-case complexity, if found, is more practical (S. Das, et al, 2013). The quantitative complexity evaluation through the procedures proposed by Suganthan, et al (2005) is platform-dependent.…”

“…In this section, we qualitatively and quantitatively evaluate the computational complexity of ADOLPSO through theoretical analysis (S. Das, Biswas, & Kundu, 2013) and the procedures recommended by Suganthan, et al (2005). The theoretical analysis discards hardware factors, such as operating systems, programming languages, and compilers used to execute the tested algorithm during the complexity evaluation process.…”

Artificial bee colony algorithm with distribution-based update rule

“…Gao et al [18] developed new search equations to adjust exploration and exploitation capability of the ABC algorithm. In a different approach for ABC algorithm, Das et al [19] proposed a learning routine based on fitness and proximity stimuli, and they tested the method on standard benchmark functions. In another study, Kiran et al proposed an integration of update rules for ABC algorithm and they analyzed the performance of proposed method on solving the numeric functions [20].…”

Artificial Bee Colony-Based Approach for Optimal Capacitor Placement in Distribution Networks

“…The algorithm has a well-balanced exploration and exploitation ability. Recent enhancements of ABC have been proposed [32][33][34] to improve performance of sharing information between artificial bees and to enhance its performance. A bee carrying out random search is called scout.…”