Abstract
The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.
Highlights
The Fireworks Algorithm (FWA) [1] is a newly developed evolutionary algorithm
The fireworks algorithm has been applied to many practical optimization problems [2], the application areas include the factorization of a non-negative matrix [3], the design of digital filters [4], the parameter optimization for the detection of spam [5], the reconfiguration of networks [6], the mass minimization of trusses [7], the parameter estimation of chaotic systems [8], the scheduling of multi-satellite control resources [9], etc
To assess the performance of improved fireworks optimization algorithm (IFWA), it is compared with FWA [8], enhanced fireworks algorithm (EFWA) [10], dynamic search firework algorithm (dynFWA) [12] and
Summary
The Fireworks Algorithm (FWA) [1] is a newly developed evolutionary algorithm. Like other evolution algorithms, it aims to find the vector with the best (usually minimum) fitness in the search space. Firstly we use the opposition-based learning strategy to initialize population to increase the diversity of the population and improve the probability to search the global optimal solution. A new explosion amplitude mechanism for the optimal firework is proposed from the aspects of population evolution rate and population aggregation degree, to enhance the ability to search the global optimal solution of the optimal firework. Adaptive t-distribution for non-optimal fireworks and elite opposition-based learning for optimal firework are applied, in order to improve the global exploration ability and local development ability and make the FWA jump out of the local optimum effectively. An improved fireworks optimization algorithm (IFWA) is proposed to improve the convergence speed and precision and reduce the run-time.
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