Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning by Xiao-bo SHI, Yin ZHANG, Shan ZHAO, Deng-ming XIAO

“…Although multiple mobile agents(MA)collaboration can quickly and efficiently complete data aggregation in wireless sensor network,the MA carrying data packages extensively increase along with a raise in the number of data source nodes accessed by MA,which causes unbalanced energy load of sensor nodes,high energy consumption of partial source nodes,and shortened lifetime of networks.The existing related works mainly focus on the objective of decreasing total energy consumption of multiple MA,without considering that rapidly energy consumption of partial source nodes has a negative effect on networks lifetime.Therefore,discrete multi-objective optimization of particle swarm algorithm was proposed,which used the total network energy consumption and mobile agent load balancing as fitness function for the approximate optimal itinerary plan in multiple mobile agent collaboration.Furthermore,the simulation result of the proposed algorithm is better than the similar algorithm in total energy consumption and network lifetime.…”
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Asset management using an extended Markowitz theorem by Paria Karimi

“…The resulted model is an NP-Hard problem and the proposed study uses two metaheuristics, namely genetic algorithm (GA) and particle swarm optimization (PSO) to find efficient solutions. …”
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Quantum Snowflake Algorithm (QSA): A Snowflake-Inspired, Quantum-Driven Metaheuristic for Large-Scale Continuous and Discrete Optimization with Application to the Traveling Salesma... by Zeki Oralhan, Burcu Oralhan

“…The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. …”
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Adaptive Control Parameter Optimization of Permanent Magnet Synchronous Motors Based on Super-Helical Sliding Mode Control by Lingtao Kong, Hongxin Zhang, Tiezhu Zhang, Junyi Wang, Chaohui Yang, Zhen Zhang

“…The Non-dominated Sorting Genetic Algorithm II and Multi-objective Particle Swarm Optimization are employed to effectively optimize control parameters, thereby mitigating motor torque and speed overshoot. …”
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