A novel numerical optimization algorithm inspired from garden balsam
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SMART DATA AGGREGATION INSPIRED PARADIGM & APPROACHES IN IOT APPLNS
A novel numerical optimization algorithm inspired from garden balsam Shengpu Li1,2 • Yize Sun3 Received: 26 September 2018 / Accepted: 16 November 2018 Ó Springer-Verlag London Ltd., part of Springer Nature 2018
Abstract This paper introduces a new evolutionary computing method inspired by the seed transmission process of garden balsam. Garden balsam, a beautiful and attractive flower, randomly ejects the seeds within a certain range by virtue of mechanical force originating from cracking of mature seed pods, which is different from natural expansion of most species of plants. The seeds scattered to suitable growth area will have greater reproductive capacity in the next generation, followed by iteration until the most suitable point for growth in a particular space is eventually found. This phenomenon can more intuitively show the process of searching the problem solution space in the optimization problem. The garden balsam optimization algorithm proposed in this paper incorporates two different types of search processes and has a mechanism to maintain population diversity. Through the optimization experiment on 24 constrained optimization problems, the results obtained by using this algorithm are compared with those of some known meta-heuristic search algorithms. The statistical analysis of the experimental results has been implemented by Friedman rank test and Holm–Sidak test. The comparison results verify the effectiveness of the algorithm. Keywords Artificial intelligent Evolutionary computing Swarm intelligence Garden balsam optimization algorithm Function optimization
1 Introduction Optimization problem runs through all aspects of human activity. Optimization idea is invariably demonstrated from the division of labor in primitive hunting, to the intensive cultivation in agricultural production, and to job scheduling in industrial production [1]. Early optimization mainly relied on empirical analysis. With the improvement in the knowledge level, people began to resort to more accurate mathematical methods to describe and solve optimization
& Yize Sun [email protected] 1
College of Information Science and Technology, Donghua University, Shanghai 201620, China
2
College of Information Engineering, Pingdingshan University, Pingdingshan 467002, China
3
College of Mechanical Engineering, Donghua University, Shanghai 201620, China
problems [2]. Since the twentieth century, new means for optimization has been available thanks to rapid development of electronic computer technology and artificial intelligence technology, enabling people to effectively deal with many complex optimization problems that could not be solved in the past, thus greatly promoting social progress and development [3]. During research on optimization problems, researchers are often inspired by nature [4]. For example, in the evolution of species, genes not adapted to the environment are gradually eliminated, while thos
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