Multi-Strategy Enhanced White Shark Optimizer for Solving Job Shop Scheduling Problem

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Swarm Intelligence

Background:

• Basic White Shark Optimizer (WSO) suffers from limited population diversity, imbalanced search mechanisms, and slow convergence.
• These limitations hinder its effectiveness in complex optimization tasks like job shop scheduling.

Purpose of the Study:

• To propose and evaluate an Improved White Shark Optimizer (IWSO) that addresses the limitations of the basic WSO.
• To enhance the performance of swarm intelligence algorithms for job shop scheduling problems.

Main Methods:

• Introduced Tent chaotic map for population initialization.
• Implemented adaptive nonlinear convergence factor and dynamic inertia weight adjustment.
• Integrated Levy flight perturbation and elite opposition-based learning.

Main Results:

• IWSO demonstrated significant superiority over seven other algorithms on the CEC2017 test suite.
• Achieved better results in minimum makespan, average convergence value, standard deviation, and successful convergence rate.
• Exhibited a leading and smoother convergence trend throughout the iteration process.

Conclusions:

• The proposed IWSO effectively overcomes the defects of the basic WSO.
• Significantly improves solution accuracy and convergence efficiency for job shop scheduling.
• Shows potential for future applications in multi-objective and dynamic scheduling problems.