Dynamic Search Fireworks Algorithm with Adaptive Parameters

Dynamic Search Fireworks Algorithm with Adaptive Parameters

Chibing Gong (Guangdong Technical College of Water Resource and Electrical Engineering, Guangzhou, China)
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJACI.2020010107

Abstract

As a comparatively new algorithm of swarm intelligence, the dynamic search fireworks algorithm (dynFWA) imitates the explosion procedure of fireworks. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added in this present study, called dynamic search fireworks algorithm with adaptive parameters (dynFWAAP). In this novel dynFWAAP, a self-adaptive method is used to tune the amplification coefficient Ca and the reduction coefficient Cr for fast convergence. To balance exploration and exploitation, the coefficient of amplitude α and the coefficient of sparks β are also adapted, and a new selection operator is proposed. Evaluated on twelve benchmark functions, it is evident from the experimental results that the dynFWAAP significantly outperformed the three variants of fireworks algorithms (FWA) based on solution accuracy and performed best in other four algorithms of swarm intelligence in terms of time cost and solution accuracy.
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However, there are deficiencies within the FWA approach. If the optimal value of the function is situated at the origin, the FWA converges to the origin very well. When the optimal value is not at the origin, it is difficult for the FWA to get the correct solution. In addition, the time cost in the selection phase of the FWA is long compared with other optimization algorithms. Therefore, the enhanced fireworks algorithm (EFWA) was introduced to enhance FWA (Zheng, 2013). The explosion size of the best firework tends to zero in EFWA, thus a new minimal amplitude check is employed. The magnitude is calculated based on the maximum number of evaluations, which does not take into account the adaptability and dynamics around the core firework. Thus, the dynFWA introduced a dynamic amplitude that will allow quick convergence or will decrease to narrow the local search (Zheng, 2014).

A new cooperative framework for fireworks algorithm (Zheng, 2015) uses the independent choice operator to improve the exploitation capacity of non-core fireworks and adopts a cooperative strategy of crowdedness-avoiding to increase the exploration capacity among fireworks. A new guiding spark in FWA (GFWA) was proposed to enhance the information utilization (Li, 2017). GFWA uses information of objective functions obtained from sparks to build a guiding vector, and to produce an elite solution. To further improve the performance of GFWA, two strategies are proposed, such as weight-based guiding strategy and quantitative increase strategy (Li, 2019).

The amplification coefficient IJACI.2020010107.m01 and reduction coefficient IJACI.2020010107.m02 in dynFWA are recommended empirically to be a value of 1.2 and 0.9, but our thorough experiments show the values of IJACI.2020010107.m03 and IJACI.2020010107.m04 should vary depending on the actual problems. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added and dynFWAAP is proposed.

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