Artificial Intelligence Optimization Techniques, Artificial Bee Colony (ABC) Algorithm and Its Global Reflections

Optimization deals with finding the best solution within complex, high-dimensional, and oftenconstrained search spaces. Optimization methods can be solved using traditional analyticalmethods which employ mathematical procedures to guarantee optimality but are usuallylimited in applicability to non-linear, non-differentiable or non-convex problems. To avoidthese limitations, nature-inspired heuristic methods are utilized to generate acceptablesolutions. Among nature-inspired meta-heuristics, Swarm Intelligence (SI)-based methodsstand out for their scalability, flexibility, and applicability due to their decentralized and self-organizing abilities. Multiple interactions, positive and negative feedback mechanisms,stochastic fluctuations in SI-based systems lead to collective intelligence to effectively searchdiscrete, high-dimensional and complex solution space. One of the prominent examples of SI-based algorithms, the Artificial Bee Colony (ABC) algorithm mimics the foraging behavior ofhoneybee colonies. In ABC, there are three distinct types of agents: employed bees whichexploit discovered high-quality solutions and share their information by dancing to recruitother bees to promising regions; onlooker bees search the vicinity of solutions selectedprobabilistically based on the information transmitted through dancing; and scout bees whichexplore new food source regions to bring diversity to the population. By iteratively balancingexploration and exploitation through self-organization mechanisms, ABC has been applied toa wide range of complex problems successfully in engineering, computer science, andcomputational biology.