Opposition-based quantum firework algorithm for continuous optimisation problems

A novel intelligence algorithm for continuous optimisation problem is proposed in this paper, termed as opposition-based quantum firework algorithm (OQFA). The proposed OQFA combines fireworks algorithm (FA) and two improved operators: opposition-based learning and quantum computing theory. The opposition-based learning operator can accelerate the convergence rate of algorithm by retaining the better solution, and the quantum computing theory can ameliorate the capability of searching and enhance the exploration efficiency of the solution space. Since OQFA has the features of both opposition-based learning and quantum computing, it has a high possibility to find a global optimum and avoids premature convergence. Experimental results on five test functions show that OQFA outperforms cultural algorithm (CA), particle swarm optimisation (PSO) and FA in terms of convergence rate and convergence accuracy.