<p>The exponential rise in the demand for electrical energy brought by the world’s population growth will result in a shortage of electricity in the future. The internet-of-things (IoT) will lead to the integration of more sophisticated gadgets into homes. Demand response (DR) initiatives enable smart cities to participate actively in the power market. Which help them manage energy more effectively and fulfil the rising demand for energy. As part of our approach, we have combined the concepts of Bacterial Foraging Optimization (BFO) and Wind-Driven Optimization (WDO) to create a hybrid algorithm known as Multitask Driven Particle Honey Bee Swarm Optimization (MT-PHBSO). As a result, a strategy that makes use of MT-PHBSO has been created to manage how much power is used by smart devices in dwellings that have IOT abilities. The objectives of this scheduling strategy are to save power expenditures, improve User Comfort (UC) and lessen the Peak-to-Average Ratio (PAR). By automatically interacting with DR signals, the MT-PHBSO-based solution tackles the primary problem of consumer information constraint in reacting to charge-based DR programs. The effectiveness and efficiency of the recommended method based on MT-PHBSO have been tested through extensive simulations. The assessment criteria were PAR, UC, cost of power and energy usage. The results of the simulation show that the suggested (MT-PHBSO) based method outperforms the benchmark techniques.</p>