ABSTRACT
The increasing integration of renewable energy sources and advanced power electronics in modern electrical systems has emphasized the need for high-performance converters. Multi-objective optimization (MOO) provides a systematic approach to simultaneously improve efficiency, reduce cost, and enhance reliability in converter design. This paper reviews current methodologies for multi-objective optimization in power converters, including classical, heuristic, and evolutionary algorithms. Converter topologies, design constraints, and performance metrics are analyzed with respect to conflicting objectives. Case studies on DC–DC, DC–AC, and multi-port converters demonstrate the trade-offs involved in optimization. Emerging trends, including AI-assisted optimization and predictive reliability modeling, are highlighted. The paper concludes with insights into future research directions to achieve cost-effective, reliable, and energy-efficient power electronic systems.
KEYWORDS: Multi-objective optimization, power converters, efficiency, reliability, cost, evolutionary algorithms, renewable energy, DC–DC converters, DC–AC converters.
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