Metaheuristics represent a promising field of study that offers significant advancements in solving difficult optimization problems. Since the first metaheuristic was proposed, significant progress has been made, and new algorithms are still being proposed on a daily basis. Without a doubt, in the near future, research in this area will continue to advance. Nonetheless, there is a clear need to identify the top-performing metaheuristics that are predicted to last forever. In this chapter, we highlight a few of the exceptional and novel metaheuristics that have emerged in the last 20 years (2000–2020), in addition to the traditional ones like tabu search, genetic, and particle swarm. After the definite foundations of the new age group of metaheuristics are presented, the absence of theoretical foundations, hybrid metaheuristics, fresh research prospects, unresolved issues, and developments in parallel metaheuristics are discussed. Intractable problems are being successfully solved with a variety of metaheuristic algorithms in recent times. These algorithms are appealing because they produce the best/optimal answers in a short amount of time, even for very large problem sizes. Metaheuristic approaches have been drawn to a wide range of optimization problems, which can be single- or multi objective, continuous or discrete, constrained or unconstrained. Because of their complex behaviour, solving these problems is not an easy task. In this era of metaheuristic algorithm research, many new metaheuristics motivated by behavioural or evolutionary processes are introduced. For many unsolved benchmark problem sets, the best solutions are produced by this new wave of metaheuristic approaches. Because of the success and widespread appeal of metaheuristic studies, as well as the growing number of publications. Our focus has been on researching notable metaheuristic algorithms, which we refer to as “new generation” metaheuristic algorithms. We consider the quantity of citations received relative to the metaheuristic’s introduction year when choosing or determining the new generation metaheuristics. As a result, numerous scientists’ experimental investigations confirm the efficiency of the metaheuristic.