Abstract
Abstract Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance.
Highlights
Optimisation may be used anywhere, as engineering design or industrial design, planning different activities, etc
The results proved that Eurasian oystercatcher optimiser (EOO) is competitive against other meta-heuristic algorithms
This study proposed a new nature-inspired meta-heuristic optimisation algorithm called EOO inspired from the behaviour of Eurasian oystercatcher (EO)
Summary
Optimisation may be used anywhere, as engineering design or industrial design, planning different activities, etc. Natural animal behaviour is simulated by observing and analyzing their social behaviour, which is considered meta-heuristics based on population, such as in particle swarm optimisation (PSO) proposed by Kennedy and Eberhart [4]. Many other nature-inspired algorithms have been developed, such as marine predators algorithm [11], the ant lion optimiser [12], red deer algorithm [13], and doctor and patient optimisation algorithm [14] These studies demonstrate how to learn from the behaviour of wild animals or insects and transform their behaviour into algorithms that may be used to solve many complex problems. This study proposes a new meta-heuristic nature-inspired algorithm called Eurasian oystercatcher optimiser (EOO) inspired by the food behaviour of Eurasian oystercatcher (EO) in searching for mussels.
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