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A high-performance permanent magnet linear generator should have low mover mass, sufficient output power, high efficiency, and high power density. Simultaneously improving the multiple parameters of a linear generator is quite challenging due to conflicting parametric requirements. This paper proposes an advanced design optimization procedure combining particle swarm optimization and genetic algorithm to address conflicting requirements. A problem statement is presented regarding such requirements through mathematical model analysis. Simulation work is executed using a finite element-based platform, ANSYS/Maxwell. Particle swarm optimization is first applied to identify an effective stator configuration, where the generator with E 1 stator supplies 3.45 kW more power than the E 2 counterpart. On the contrary, despite a 36.46 % reduction in the mover mass of C-stator, its efficiency is 8.96 % higher than that of the linear generator with the E 1 stator. A genetic algorithm is then applied to optimize the initial C-cored design, yielding simultaneous enhancements of 7.12 % in power, 13.97 % in power density, and 3.03 % in efficiency, along with a 32.5 % reduction in mover mass. The workflow and outcomes are summarized through a structured optimization flowchart. • Identification of conflicting parameters through mathematical model analysis. • Proposal of an advanced methodology to improve multiple parameters simultaneously. • Improvement of multiple parameters concurrently despite reducing the mover mass.
Molla et al. (2026) studied this question.