A Q-Learning-Enhanced Cuckoo Catfish Optimizer (CCO-RL): A Comparative Study of Nine Metaheuristics Applied to CEC2017, CEC2022 and Engineering Design Problems

The Cuckoo Catfish Optimizer (CCO) is a recent swarm method with four built-in movement strategies. Its weakness is not the moves themselves but the way it chooses among them: a fixed chain of random-versus-threshold (rand>C) tests that ignores how each agent is actually doing and keeps no memory of which move has been paying off. On harder, higher-dimensional problems, this rigidity drains diversity and the search stalls. We propose CCO-RL, which hands the choice of move to a small tabular Q-learning controller. For every agent at every iteration, the controller reads a 48-state summary of the agent’s crowding, its recent stagnation and how far the run has progressed, then picks one of the four moves. A bounded reward and a decaying ε-greedy rule let it learn a policy online with no extra function evaluations. We test CCO-RL against the original CCO and eight popular metaheuristics on CEC2017 (D=30,50) and CEC2022 (D=20): 70 instances, 30 runs each. CCO-RL earns the best overall Friedman rank (1.69) and significantly beats every external competitor according to the Nemenyi test. It also finds the best mean design in three engineering problems.