Abstract
Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.
Highlights
Optimization algorithms are essential for numerous optimization applications where usually certain parameters are minimized or maximized by considering an objective function
In [37], dragonfly algorithm (DA) has been applied to the economic load dispatch problem in power systems, which consists of minimizing the generation cost while satisfying constraints such as ramp rate, demand and generator operating limit; in [38], it has been applied to the static economic dispatch problem incorporating solar energy; in [39], it has been applied to the combined economic emission dispatch problem, and in [40], it has been applied to the dynamic economic dispatch problem
The dragonfly algorithm, a recently proposed swarm intelligence algorithm, has been applied in numerous applications, and it is shown to have a higher performance as compared to other swarm intelligence algorithms
Summary
Optimization algorithms are essential for numerous optimization applications where usually certain parameters are minimized or maximized by considering an objective function. Non-deterministic algorithms, called heuristic algorithms, are being increasingly used and developed They can be based on various natural processes; for example, trajectory-based, physics-based or population-based, which can be either nature- or bio-inspired [1]. Swarm intelligence algorithms are classified as nature-inspired population-based heuristic optimization algorithms. Owing to its high effectiveness and efficiency, it has been utilized in multifarious applications and attempts to further improve its performance have been made and a number of hybrids of DA have been proposed. Our motivation for working on this algorithm is that DA and its hybrids have proven to be useful in multifarious applications and they have a higher performance as compared to other swarm intelligence algorithms and their hybrids.
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