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In this paper, a new metaheuristic algorithm called Sculptor Optimization Algorithm (SOA) is introduced and designed, which imitates the sculpting process. The main idea in SOA design is derived from (i) making extensive changes to the sculpture material and (ii) making small and detailed changes to the sculpture. SOA theory is expressed and then mathematically modeled in two phases of exploration and exploitation. The performance of SOA in handling optimization applications has been evaluated to optimize the CEC 2017 test suite. The optimization results show that SOA, with its high power in managing exploration and exploitation during the search process, has been able to achieve suitable solutions for optimization problems. In addition, the quality of SOA results has been compared with the performance of twelve well-known metaheuristic algorithms. Analysis of the simulation results shows that SOA has provided superior performance compared to competing algorithms by achieving better results for most of the benchmark functions. Simulation findings show that compared to competing algorithms, SOA has been successful in handling 100% of unimodal functions, multimodal functions and hybrid functions, as well as 70% of composite functions. [ABSTRACT FROM AUTHOR]

In this article, a new human-based metaheuristic algorithm named Dollmaker Optimization Algorithm (DOA) is introduced, which imitates the strategy and skill of the dollmaker when making dolls. The basic inspiration of DOA is derived from two natural behaviors in the doll making process (i) making general changes to the doll-making materials and (ii) making precise small changes on the appearance characteristics of the dolls. The theory of DOA is proposed and then modeled mathematically in two phases (i) exploration based on the simulation of large changes made on doll-making materials and (ii) exploitation based on the simulation of small changes on the made dolls. The performance of DOA in optimization is evaluated on twenty-three standard benchmark functions of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that DOA has achieved suitable results for optimization problems with its ability in exploration, exploitation, and balance them during the search process. Comparison of DOA results with twelve competing algorithms shows that the proposed algorithm has superior performance compared to competing algorithms by providing better results in all twenty-three benchmark functions and getting the rank of the first best optimizer. In addition, the efficiency of DOA in handling real-world applications is evaluated in the optimization of four engineering design problems. Simulation results show that DOA has acceptable performance in real world and engineering applications by providing better values for design variables and objective functions compared to competing algorithms. [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]