Diversity and Mechanisms in Swarm Intelligence

Diversity and Mechanisms in Swarm Intelligence

Xin-She Yang (School of Science and Technology, Middlesex University London, London, UK)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/ijsir.2014040101
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Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.
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2. Infinite Monkey Theorem And Swarm Intelligence

There is a well-known thought experiment, called the infinite monkey theorem, which states that the probability of producing any given text will almost surely be one if an infinite number of monkeys randomly type for an infinitely long time (Marsaglia, 1993; Gut, 2005). In other words, the infinite monkeys can be expected to reproduce the whole works of Shakespeare. For example, to reproduce the text “algorithm” (9 characters), for a random sequence of n characters on a 101-key computer keyboard, the probability of a consecutive 9-character random string to be “algorithm” is p=(1/101)9 ≈ 8.4 × 10-19, which is extremely small. However, the importance here is this probability is not zero. Therefore, for an infinitely long sequence n→∞, the probability of reproducing the collected works of Shakespeare is one, though the formal rigorous mathematical analysis requires Borel-Cantelli’s lemma (Marsaglia & Zaman, 1993; Prokhorov, 2002; Gut, 2005).

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