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

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Evolutionary Computation

Background:

• Multi-objective optimization problems (MOPs) involve balancing conflicting goals.
• Existing algorithms face challenges in maintaining solution diversity and convergence.
• The behavior of crested porcupines inspires novel optimization approaches.

Purpose of the Study:

• Introduce the Multi-Objective Crested Porcupines Optimization (MOCPO) algorithm.
• Enhance convergence and diversity control in multi-objective optimization.
• Provide an effective tool for complex engineering design and benchmark problems.

Main Methods:

• Developed MOCPO based on the Crested Porcupines Algorithm, incorporating elitist, non-dominated sorting, and crowding distance mechanisms.
• Integrated a novel Information Feedback Mechanism (IFM) and an enhanced solution updating strategy.
• Evaluated MOCPO on ZDT, DTLZ, and RWMOP benchmark suites, comparing against state-of-the-art algorithms.

Main Results:

• MOCPO demonstrated significant improvements in convergence speed and solution diversity.
• Qualitative and quantitative analyses confirmed the algorithm's effectiveness on diverse problem types.
• MOCPO outperformed established algorithms like MOGBO, Pre-DEMO, MOEDO, Pi-MOEA, and ClGrMOEA.

Conclusions:

• MOCPO is a robust and effective algorithm for tackling complex multi-objective optimization problems.
• The algorithm's design promotes superior performance in both convergence and diversity.
• MOCPO offers a viable and improved alternative for real-world engineering design challenges.