A key aspect of the design of evolutionary and swarm intelligence algorithms is studying
their performance. Statistical comparisons are also a crucial part which allows for reliable …

Moth-flame optimization (MFO) is a widely used nature-inspired algorithm characterized by a
simple structure with simple parameters. However, for some complex optimization tasks …

Abstract The Bacterial Foraging Optimization (BFO) algorithm is a swarm intelligent
algorithm widely used in various optimization problems. However, BFO suffers from multiple …

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as
SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a …

Whale optimization algorithm (WOA) is a recent nature-inspired metaheuristic that mimics
the cooperative life of humpback whales and their spiral-shaped hunting mechanism. In this …

The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The
algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the …

An appropriate threshold is a key to using the multi-threshold segmentation method to solve
image segmentation problems, and the swarm intelligence (SI) optimization algorithm is one …

An optimized design with real-time and multiple realistic constraints in complex engineering
systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node …

Artificial bee colony (ABC) algorithm is a popular meta-heuristic optimization algorithm
inspired by the foraging behaviors of honeybees. Although ABC has outstanding exploration …

Antlion optimization (ALO) is an efficient metaheuristic paradigm that imitates antlion's
foraging behavior when they search for the ants. However, the conventional variant appears …