Βrain storm optimization (BSO) is a swarm-intelligence clustering-based algorithm inspired by the human brainstorming process. Electromagnetism-like mechanism for global optimization (EMO) is a physics-inspired optimization algorithm. In this study we propose a novel hybrid metaheuristic evolutionary algorithm that combines aspects from both BSO and EMO. The proposed algorithm, named EMotion-aware brain storm optimization, is inspired by the attraction–repulsion mechanism of electromagnetism, and it is applied in a new emotion-aware brainstorming context, where positive and negative thoughts produce ideas interacting with each other. Novel contributions include a bi-polar clustering approach, a probabilistic selection operator, and a hybrid evolution process, which improves the ability of the algorithm to avoid local optima and convergence speed. A systematic comparative performance evaluation that includes sensitivity analysis, convergence velocity and dynamic fitness landscape analyses, and scalability assessment was performed using several reference benchmark functions from standard benchmark suites. The results validate the performance advantages of the proposed algorithm over relevant state-of-the-art algorithms.