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This paper introduces a novel nature-inspired optimization algorithm called the Addax Optimization Algorithm (AOA), which emulates the natural behavior of addax in the wild. The core inspiration for AOA is drawn from the addax's foraging strategy and digging skills. The theoretical foundation of AOA is expounded and mathematically modeled in two phases: (i) exploration based on modeling addax position change during foraging and (ii) exploitation based on addax position change modeling during digging. The efficiency of AOA in handling real-world engineering applications is evaluated on four engineering design problems. The optimization results show that AOA is achieved effective solutions for optimization problems with its high ability in exploration, exploitation, and establishing a balance between them during the search process. The outcomes derived from applying AOA are compared with the performance of twelve well-known optimization algorithms. The simulation results show that AOA is provided superior performance compared to competitor algorithms, by achieving better results and ranking as the first best optimizer. The simulation findings show that the proposed AOA approach has an effective performance for handling optimization tasks in engineering applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]