Abstract
With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important. Existing algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial problems. Thus, this work proposes an improved many-objective pigeon-inspired optimization (ImMAPIO) algorithm with multiple selection strategies to solve many-objective optimization problems. Multiple selection strategies integrating hypervolume, knee point, and vector angles are utilized to increase selection pressure to the true Pareto Front. Thus, the accuracy, convergence, and diversity of solutions are improved. ImMAPIO is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III, GrEA, MOEA/D, RVEA, and many-objective Pigeon-inspired optimization algorithm. Experimental results indicate the superiority of ImMAPIO on these test functions.
Highlights
Swarm intelligence algorithms are optimization simulates the predatory behavior of birds, which moves algorithms inspired by the behavior of some insects and animals in nature
The proposal of multi-objective pigeon-inspired optimization (MPIO) approach enriches the application of PIO algorithms in real life, but the non-dominance solutions have become common in many-objective optimization problems
This study proposes the improved pigeoninspired optimization algorithm for many-objective optimization problems with multiple selection strategies (ImMAPIO), which can preserve elite individuals and extend the influence of the initial population and elite individuals on velocity and position update
Summary
Pigeon-inspired optimization algorithm[13] simulates the behavior of pigeons’ spontaneous homing. To solve the problem of the standard PIO algorithm falling into the local optimum, Li and Duan[23] proposed the Simulated Annealing Pigeon-Inspired Optimization (SAPIO) algorithm with the goal of completing the target detection approach for unmanned aerial vehicles (UAVs) by using the simulated annealing mechanism and the and the edge potential function (EPF). Qiu and Duan[28] proposed a variant of PIO called multi-objective PIO, which is used in the parameter design of brushless direct current motors This method uses a Pareto sorting scheme[29] and consolidation operator to enhance the selection pressure of the individuals in a population to the true PF. Duan et al.[30] proposed a novel MPIO that uses the limit cyclebased mutant mechanism to produce new solutions and search directions This method achieves good performance in terms of the diversity and accuracy of solutions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
