A new multi objective crested porcupines optimization algorithm for solving optimization problems

This paper presents a new multi-objective optimization algorithm called the Multi-Objective Crested Porcupines Optimization (MOCPO) Algorithm, which uses an elitist, non-dominated sorting and crowding distance mechanism. MOCPO is motivated by the predator-prey behavior of crested porcupines and is based on the newly proposed Crested Porcupines Algorithm. MOCPO is formulated to efficiently manage conflicting objectives in multi-objective optimization problems. Through the use of non-dominated sorting and crowding distance mechanisms, MOCPO promotes solution diversity and convergence towards the Pareto front. MOCPO employs a new Information Feedback Mechanism (IFM) and an enhanced solution updating strategy to enhance convergence and diversity control. The performance of MOCPO is tested on a variety of benchmark problems, including the ZDT and DTLZ series, as well as real-world engineering design problems from the RWMOP suite. These test problems represent a variety of optimization problems with linear, nonlinear, continuous, and discrete nature. MOCPO performance is compared with state-of-the-art algorithms like the Multi-Objective Gradient Based Optimizer (MOGBO), Preference inspired Differential Evolution (Pre-DEMO), Multi-Objective Exponential Distribution Algorithm (MOEDO), Pivot solution based Multi-Objective Evolutionary Algorithm (Pi-MOEA), and Clustering aided Grid based Multi-Objective Evolutionary Algorithm (ClGrMOEA). Qualitative and quantitative analyses using standard performance metrics show the effectiveness of the algorithm. Experimental results verify that MOCPO provides substantial improvements in convergence and solution diversity, making it a viable choice for solving complex multi-objective optimization problems.