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Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state‐of‐the‐art variant of PSO. Convergence analysis reveals that DiPSO achieves faster convergence and superior solutions. These results highlight the effectiveness of DiPSO in solving optimization problems. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The Spiral Optimization Algorithm (SOA) is an optimization technique developed recently (2011) by K. Tamura and K. Yasuda at Tokyo Metropolitan University-Japan. SOA is a metaheuristic based on an analogy of spiral phenomena in nature and it is simple in concept, few in parameters and easy in implementation. In this paper, SOA is proposed for solving the Combined Economic and Emission Dispatch (CEED) problem. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. The CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor. The proposed algorithm has been implemented on three test systems with 3, 6, and 40 generating units, with different constraints and various cost curve nature. In order to see the effectiveness of the proposed algorithm, its results are compared to those reported in the recent literature. Those results are quite encouraging showing the good applicability of SOA for CEED problem. [ABSTRACT FROM AUTHOR]