Recent Advances of Nature-Inspired Metaheuristic Optimization

Metaheuristic approaches receive a great interest in the area of optimization, especially when exact methods are missing, or the cost is extremely high. Besides the possibility to report good solutions in reasonable time, metaheuristic techniques are widely applicable. There are diverse categories of techniques that differ in number of search agents (or solutions), solution representation, and movement mechanism in search space. Just mentioned ingredients are determined according to the motivation or inspiration philosophy behind the technique. Nature-inspired optimization category is very popular and has proven high efficiency in many problems. It contains famous subclasses like evolutionary algorithms, swarm intelligence, and single-based techniques. Famous and classical examples of each subclass are genetic algorithm, particle swarm, and ant colony optimization, and simulated annealing, respectively. Nature-inspired optimization family grows so fast, and many members have joined it recently, for example, emperor penguin colony (2019), seagull optimization algorithm (2019), sailfish optimizer (2019), pity beetle algorithm (2018), emperor penguin optimizer (2018), multi-objective artificial sheep algorithm (2018), salp swarm algorithm (2017), electromagnetic field optimization (2016), sine cosine algorithm (2016), moth-flame optimization (2015), grey wolf optimizer (2014), flower pollination algorithm (2012), bat algorithm (2010), cuckoo search algorithm (2009), firefly algorithm (2008), and many others. There are many proposed hybridization and cooperation methods between techniques to produce improved versions of original ones. Nature-inspired techniques have been used in many application areas like theoretical computer science, engineering and control, forecasting, medical field, finance, management, operation research, and others. Also, new scientific disciplines like renewable energy, robotics, and navigation are feasible areas to make use of nature-inspired techniques. This chapter sheds light on six so recently new techniques that belong to nature-inspired optimization class.

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