A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Political Optimizer (PO) has limited global exploration due to few subgroup optimal solutions.
• PO's recent past-based position updating strategy (RPPUS) lacks candidate solution verification, slowing convergence.

Purpose of the Study:

• To introduce GPOFWA, a novel hybrid algorithm integrating PO and FWA.
• To improve the global exploration and convergence speed of the Political Optimizer.

Main Methods:

• Hybridization of Political Optimizer (PO) with Fireworks Algorithm (FWA) using spark explosion mechanisms.
• Incorporation of Gaussian explosion sparks for candidate solution correction after RPPUS.
• Introduction of a new subgroup optimal solution, Converged Mobile Center (CMC), to maintain population diversity.

Main Results:

• GPOFWA demonstrated superior performance on benchmark functions (30 well-known, CEC2019) and engineering problems.
• The hybrid approach significantly enhanced the exploitative ability and convergence speed.
• Experimental results confirmed GPOFWA's effectiveness compared to state-of-the-art methods.

Conclusions:

• GPOFWA effectively addresses the limitations of the original PO.
• The proposed algorithm offers improved solution quality for numerical and engineering optimization tasks.
• GPOFWA represents a significant advancement in hybrid optimization techniques.