Journal Article10.1016/j.asoc.2023.110925
Adaptive multi-objective competitive swarm optimization algorithm based on kinematic analysis for municipal solid waste incineration
W. Huang,Haixu Ding,Junfei Qiao +2 more
7
TL;DR: This study proposes an adaptive multi-objective competitive swarm optimization algorithm for municipal solid waste incineration, achieving 4.36% heat improvement and 4.13% exhaust gas reduction through kinematic analysis and multi-strategy learning.
read more
Abstract: Multi-objective optimization for the municipal solid waste incineration process is considered as a valuable technique to improve energy recovery and reduce pollutant emission. However, the complex mechanism analysis and multimodal problem of the municipal solid waste incineration process set challenges for both the modeling and optimization studies. To overcome this problem, an adaptive multi-objective optimization for the municipal solid waste incineration process is proposed in this paper. First, a bi-objective model of the municipal solid waste incineration, the basis for optimization, is established based on mass balance and energy balance, which takes furnace temperature and flue gas oxygen content as decision variables to mathematically deduce the generated heat and exhaust gases. Second, an adaptive multi-objective competitive swarm optimization algorithm is proposed for the optimization of the municipal solid waste incineration process. Two-step competition and multi-strategy learning are designed to provide a clear division of labor for particles and a novel idea for detecting evolutionary environment is proposed based on kinematic analysis of particles. Finally, relevant experiments are conducted on benchmark instances and the municipal solid waste incineration optimization model. The proposed algorithm shows promising convergence, diversity, fastness by comparing with several representative and state-of-the-art algorithms. The proposed algorithm achieves the optimization effects with 4.36% improvement of the available heat for power generation and 4.13% reduction of the exhaust gas assessment in the optimization of the municipal solid waste incineration process.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Competitive Swarm Optimizer: A decade survey
Dikshit Chauhan,Shivani,Ran Cheng +2 more
TL;DR: This paper reviews a decade of Competitive Swarm Optimizer (CSO) research, synthesizing key variants, principles, and applications, including single-objective, multi-objective, and engineering optimization problems, and provides a taxonomy and comparative analyses for future investigation.
12
Waste tire valorization: Advanced technologies, process simulation, system optimization, and sustainability
Hu Yusha,Xiaopeng Yu,Jingzheng Ren,Zhiqiang Zeng,Qian Qiming +4 more
TL;DR: Waste tire valorization involves advanced technologies, process simulation, system optimization, and sustainability. It aims to convert waste tires into valuable products and energy, promoting resource recycling and mitigating environmental harm.
7
Influence of main operating parameters on the incineration characteristics of municipal solid waste (MSW)
Wuqing Zeng,Yu Wang,Qingguo Bu,Shuo Ma,Haoran Hu,Dandan Ma,Hongting Ma +6 more
TL;DR: This study investigates the effects of primary air temperature, secondary air arrangement, and grate speed on municipal solid waste incineration, revealing optimal operating conditions and influencing factors, including moisture evaporation, fixed carbon combustion, and volatile release rates.
6
Evolving Dual-Directional Multiobjective Feature Selection for High-Dimensional Gene Expression Data
Yunhe Wang,Xiaomin Li,Zhenggui Du,Wenyuan Xiao,Hongpu Liu,Liang Yang +5 more
- 01 Jan 2024
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
SPEA2: Improving the strength pareto evolutionary algorithm
Eckart Zitzler,Marco Laumanns,Lothar Thiele +2 more
- 01 Jan 2001
TL;DR: An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
6K
•Book
What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050
Silpa Kaza,Lisa Congyuan Yao,Perinaz Bhada-Tata,Frank Van Woerden +3 more
- 12 Dec 2018
TL;DR: The What a Waste 20: A Global Snapshot of Solid Waste Management to 2050 as discussed by the authors aggregates extensive solid waste data at the national and urban levels and provides information on waste management costs, revenues, and tariffs; special wastes; regulations; public communication; administrative and operational models; and the informal sector
4.7K
The Arithmetic Optimization Algorithm
Laith Abualigah,Ali Diabat,Ali Diabat,Seyedali Mirjalili,Mohamed Abd Elaziz,Mohamed Abd Elaziz,Amir H. Gandomi +6 more
TL;DR: Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms.
2.2K