Q PSO: Constrained Environment for Economic Dispatch

In this era of globalization, enterprises require economic dispatch within constrained environments to ensure business sustainability and long-term success. These constraints may include legal regulations, industry standards, ethical codes, and social responsibility requirements. When facing these limitations, organizations need to develop appropriate economic dispatch strategies that balance stakeholder interests, maximize profits, ensure regulatory compliance and social responsibility, while simultaneously considering employee rights protection and corporate reputation enhancement. The Q PSO algorithm implementation typically incorporates constraint-handling mechanisms through penalty functions or feasibility rules, where infeasible solutions are either penalized in the objective function or repaired to satisfy constraints. For optimal performance, we recommend using high-version MATLAB (R2019b or later) which offers enhanced optimization toolbox features, improved parallel computing capabilities for large-scale problems, and advanced constraint management through functions like fmincon with sequential quadratic programming (SQP) algorithms. This ensures enterprises maintain competitiveness and viability in evolving markets through efficient resource allocation and constraint-aware optimization.