Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Abstract

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator’s attack on a pufferfish and (ii) exploitation based on the simulation of a predator’s escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

“…PSO models the group movement of birds or fish searching for food, ACO is inspired by ants finding the shortest communication path, ABC mimics honey bees’ activities in locating food, and the FA replicates fireflies’ optical communication. Noteworthy wildlife activities, such as foraging, hunting, chasing, migration, and digging, serve as the foundation for swarm-based metaheuristic algorithms like the Pufferfish Optimization Algorithm (POA) [ 15 ], Golden Jackal Optimization (GJO) [ 16 ], Tunicate Swarm Algorithm (TSA) [ 17 ], Coati Optimization Algorithm (COA) [ 18 ], Chameleon Swarm Algorithm (CSA) [ 19 ], Wild Geese Algorithm (WGA) [ 20 ], White Shark Optimizer (WSO) [ 21 ], Grey Wolf Optimizer (GWO) [ 22 ], African Vultures Optimization Algorithm (AVOA) [ 23 ], Mantis Search Algorithm (MSA) [ 24 ], Marine Predator Algorithm (MPA) [ 25 ], Whale Optimization Algorithm (WOA) [ 26 ], Orca Predation Algorithm (OPA) [ 27 ], Reptile Search Algorithm (RSA) [ 28 ], Honey Badger Algorithm (HBA) [ 29 ], and Kookaburra Optimization Algorithm (KOA) [ 30 ].…”

Harmonics management and hosting capacity enhancement: Optimal double-resistor damped double-tuned power filter with artificial hummingbird optimization

“…In summary, MOAHA provides a powerful swarm intelligence-based multi-objective optimizer that takes inspiration from hummingbirds evolved foraging behaviors and flight dynamics. Its performance and expandability by integrating intelligent solution recommendation methods highlights the promise of bio-inspired algorithms [34].…”