# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 3 Challenges, Research Gaps, and Issues
### Focus in Developing New Algorithms Versus \"No Focus in Consolidation\"  
Due to the large number of algorithms, it gradually became clear that part of these algorithms does not necessarily lead to suitable optimization methods [15]. Thereby, some researchers have started to criticize the excess of new metaheuristics algorithm publications [12; 13; 15; 57]. The main argument lies in the overvaluation of the source of inspiration. Some researchers tend to be more concerned with the algorithm's metaphor and novelty than with their quality as optimization methods.  
[PERSON] [15; 57] called this phenomenon of \"metaphor fallacy\" he expects that this mindset turns quickly. He argued that _most \"novel\" metaheuristics based on a new metaphor take the field of metaheuristics a step backward rather than forward_. Therefore, the community must be more concerned about developing more efficient methods and avoid the \"metaphor fallacy\" trap.  
Many researchers also argue that the metaheuristics' behaviors are more important than the source of inspiration [12; 19; 92]. Generally, the metaphor is more ludic and generally does not explain the real functioning of the algorithm. Because of this, the metaphor used may be difficult to understand the algorithm's proposal because it can overshadow the actual functioning.  
Furthermore, these new algorithms (the novelty) are often an extension of existing algorithms with little functional alterations [16; 91; 92]; they can be seen as variants, and the metaphors are highlighted as the novelty. [PERSON] et al., 2020 [12], also analyzed the most influential algorithms in their review, and they state that five algorithms (their standard implementations) influenced the majority of other algorithms. According to them, the algorithms most influential are (1) Genetic Algorithms (GA), (2) Particle Swarm Optimization (PSO), (3) Ant Colony Optimization (ACO), (4) Differential Evolution (DE), and (5) Artificial Bee Colony (ABC).  
This issue must highlight two aspects. Firstly, this research production trend may be related to the existing publication mindset, where the chances to publish a new algorithm are high. [PERSON] et al., 2021 [91], pointed out that the intensity of Combinatorial Optimization grew exponentially since 1989. Secondly, obviously, the new algorithms were crucial to the development of the metaheuristics field. All the proposed metaphors helped to bring different ways to explore the search space, such as anarchy societies, ants, bees, black holes, cats, cuckoos, consultants, clouds, dolphins, fishes, flower pollination, gravity,water drops, and so on so forth. The critic is related to the overestimation of the source of inspiration as a primary driver with few concerns whether it effectively presents novelty for optimization methods (innovation in search strategy perspective).  
This issue is an open question in the past few years, at least since 2013 when [PERSON] published \"Metaheuristic--the metaphor exposed\" [57] and the attention for this has increased. It is a research mindset that requires change because, in some sense, the progress and consolidation of the metaheuristics field are difficult. The answer for this challenge may demand a methodology to define COP and MH and provide a fair way to compare metaheuristics features, strategies, and their performance.