Paper Title
Whale Optimization Algorithm and its Application: A Survey

Abstract
A group of artificial intelligence (AI) computing methods known as "soft computing" give machines human-like problem-solving abilities. In contrast to meta-heuristics, which are general-purpose, problem-independent optimization algorithms, heuristics are problem-dependent algorithms tailored to the specifics of a certain optimization problem. They pertain to a variety of issues and issue occurrences. One of the primary categories of nature-inspired algorithms, swarm intelligence (SI), is created by mimicking the social behavior of some basic creatures. The whale optimization technique is a meta-heuristic algorithm with biological inspiration based on how humpback whales hunt in groups. Exploration of the search space should be more varied, and the best agent should be more focused (exploitation).Exploration (diversification of the search space) and exploitation (intensification of the best agent) are two opposing ideas that must be taken into account while creating or using a meta-heuristic algorithm. As a result, maintaining a steady balance between exploitation and exploration inclinations is a must when utilizing swarm-based optimizers and can frequently be improved. The algorithm experiences early convergence, which traps it in local optimums. An enhanced whale optimization technique is suggested to get over WOA's restriction. The bubble-net hunting technique serves as the basis for the algorithm. Dedicated the increasing complexities of models and the requirement for speedy engineering decisions, Meta-heuristics have received engineering and scientific interest for the purpose of decision-making. Keywords - Artificial Intelligence, Heuristic Algorithms, Meta-Heuristic Algorithms, Swarm Intelligence