Paper Publishing Based Optimization: A New Human-Based Metaheuristic Approach for Solving Optimization Tasks

In this paper, a new human-based metaheuristic algorithm called Paper Publishing Based Optimization (PPBO) is introduced, which imitates the process of paper publication.The fundamental inspiration of PPBO is derived from the efforts of authors to improve their papers based on the comments of reviewers and editors in order to gain acceptance.The theory of PPBO is described, then it is mathematically modelled based on the simulation of process of paper publication.The performance of PPBO in handling optimization problems is evaluated on twenty-three standard benchmark functions of unimodal, high dimensional multimodal, and fixed dimensional multimodal.The optimization results show that PPBO has a reliable ability in exploration, exploitation, and balance them in the search process.The effectiveness of PPBO in the optimization process is compared with the performance of twelve wellknown metaheuristic algorithms.The simulation results show that PPBO has superior performance compared to competitor algorithms by providing better results in most of the benchmark functions.