Abstract
AbstractAs a novel global optimization algorithm, the fruit fly optimization algorithm FOA has been successfully applied in a variety of mathematic and engineering fields. For the purpose of accelerating the convergence speed and overcoming the shortcomings of FOA, an improved fruit fly optimization called SEDI-FOA was proposed in this paper. In the proposed SEDI-FOA, more fruit flies would fly in the search direction that was best for finding the optimal solution, or at least in a direction close to the optimal direction. Experiments were conducted on a set of 12 benchmark functions, and the results showed that SEDI-FOA performed better than other several improved FOA and frequently-used intelligence algorithms, especially in the areas of accelerating convergence and global search ability and efficiency.
Highlights
Optimization problems are used extensively in science, engineering and business
Fruit fly optimization algorithm (FOA) is a new novel optimization algorithm presented by the scholar Pan [1][3], which is inspired by the food searching behavior of fruit-fly, and has the advantage of being easy to understand, much simpler and more robust compared with the complicated optimization methods proposed by past scholars [1][3]
The best fruit fly flying routes for benchmark functions in this simulation using the proposed SEDIFOA approach are illustrated in Fig. 4, which shows that the best fruit fly can direct the global optimal solution in an efficient way
Summary
Optimization problems are used extensively in science, engineering and business. Over the last few decades, a number of meta-heuristic optimizations have been developed. These optimizations are based on biotic factors [1] [2]. Fruit fly optimization algorithm (FOA) is a new novel optimization algorithm presented by the scholar Pan [1][3], which is inspired by the food searching behavior of fruit-fly, and has the advantage of being easy to understand, much simpler and more robust compared with the complicated optimization methods proposed by past scholars [1][3]. The algorithm can be widely used in science and engineering fields [4], such as in solving the steelmaking casting problem [5], continuous mathematical function optimization problems [6], GRNN parameters optimization [7], semiconductor final testing scheduling problem [8], web auction logistics service [9], design of the PID controller [10], power load forecasting [11], multidimensional knapsack problem optimization [12] and analyzing swarms of mini autonomous surface vehicles [13]
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