Multi-objective Particle Swarm Optimization Model Research Articles

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

In recent decades, there has been a surge in demand for the development of renewable energies, leading to extensive research efforts focused on the gasification process. Consequently, a wide range of studies has been conducted to explore different feedstocks for gasification, with particular emphasis on plastic waste. Although a wide range of studies conducted various optimization methods for the gasification process, there was no specific study to employ metaheuristic methods in the field of plastic waste gasification. This study aimed to perform a multi-objective particle swarm optimization (MOPSO) method to solve a multi-objective plastic waste gasification optimization problem for enhancing energetic and exergetic viewpoints. Moreover, exact methods including non-linear optimization and response surface methodology (RSM) were conducted to be compared with the results derived from MOPSO. Pareto-front solutions obtained from MOPSO were ranked using grey relational analysis (GRA) and the optimum responses were 517 kJ/kg, 84 %, and 57 % for lower-heating value, cold gas efficiency, and exergy efficiency, respectively, which were compared to optimum options obtained from multi-objective non-linear optimization and RSM techniques. While the MOPSO model may not have performed as well as other methods in this particular problem, it is important to acknowledge its capabilities. Future research can concentrate on improving the behavior and performance of the MOPSO method when dealing with a larger number of points or when applied to different problem domains.

Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios

Multiobjective Algorithm for Urban Land Spatial Layout Optimization

In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%.

Sequencing mixed-model assembly lines with demand management: problem development and efficient multi-objective algorithms

As a kind of production line, the mixed-model assembly line (MMAL) has been progressively adopted by industries to satisfy the diversification of customer demands. This study applied an MMAL sequencing problem for a make-to-order (MTO) strategy. Using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach, this study sorted customer orders, satisfying these by a desired due date being the main feature in MTO systems. This MMAL problem was investigated to achieve three minimization objectives: total set-up cost, number of work overload situations, and total earliness and tardiness costs, according to the specified priority of orders. Accordingly, a multi-objective particle swarm optimization (MOPSO) algorithm was developed for the proposed NP-hard model and the algorithm parameters were tuned by applying the Taguchi technique to the experimental design. The computational results of a comparison between the proposed MOPSO model and four multi-objective algorithms confirmed its advantages.

Optimization of Water Distribution Network Design for Resisting Cascading Failures

Water distribution networks (WDNs) is crucial to ensure social operations and economic activities. However, WDNs are highly sensitive and vulnerable to disasters. The aim of this study is to mitigate the catastrophic consequences of cascading failures in WDNs. A flow-based WDN cascading failure model is built. The extended multi-objective particle swarm optimization model is developed to resist cascading failures and improve resilience. This model takes pipe diameter as the decision variable to minimize cost and maximize pressure deficit. Water balance, pressure, and standard pipe diameter are the constraints. The classical optimal scenario (COS) and the cascading failure scenario (CFS) are simulated. The model is applied to a small and medium-sized benchmarked WDN. Results show that the extended PSO can find the optimal solution on the benchmarked WDN. The Pareto fronts are obtained. Compare to the Pareto fronts between COS and CFS, the pressure deficit under CFS is significantly reduced, and the cost is reduced while the same pressure deficit increased. Different tolerance parameters are tested. The small network is not sensitive to the tolerance parameter, but the medium-sized network is sensitive. The model evaluates a variety of conflicting goals, which help designers and water managers resist cascading failures in WDNs.

Multi-objective particle swarm optimization model for conjunctive use of treated wastewater and groundwater

In this research, cropping pattern optimization models have been developed to maximize the benefits and minimize the potential negative effects of quantitative-qualitative conjunctive use of unconventional surface water (treated wastewater) and groundwater in agricultural irrigation. Three objective functions were considered: maximizing the benefits from crop patterns, reducing nitrogen leaching, and improving the rate of aquifer recharge. The developed models use particle swarm optimization (PSO) integrated with an additive weighting method and a multi-objective particle swarm optimization (MOPSO) algorithm for different single- and three-objective optimization scenarios in the Varamin irrigation network in Iran, and then the results of the models were compared. The solutions resulting from the three-objective model using the PSO algorithm with the additive weighting method indicated that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 49%, and 20%, respectively. Meanwhile, the conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 35% and 88%, respectively. The solutions resulting from the three-objective model using the MOPSO algorithm, forming Pareto front, and then using TOPSIS to select the optimal solution from among the non-dominated solutions showed that the benefits obtained from optimizing cropping patterns, water consumption productivity, and aquifer recharge were increased by 7%, 47% and 15%, respectively. The objectives of conjunctive use of treated wastewater and groundwater and the consumption of nitrogen fertilizer were decreased by 36% and 89%, respectively. The difference between the objective function values of the two algorithms is about 0.4% to 4%, which shows the proximity of the models in finding the optimal solution. The results of this research can be used to optimize the use of water resources, increase farmers’ benefits, and decrease nitrogen leaching from irrigation and drainage networks.

Sustainable Land-Use Allocation: A Multiobjective Particle Swarm Optimization Model and Application in Changzhou, China

AbstractThe harmonization of environmental protection with land use for socioeconomic development is one of the major challenges that most Chinese cities are facing in the rapid urbanization and in...

Optimal waste load allocation using graph model for conflict resolution.

In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran.

Study on Dynamic Reconfiguration of Distribution Network Considering Distribution Generation

A new dynamic reconfiguration method considering distribution generation (DG) was presented. The dynamic reconfiguration problem was solved from three aspects: firstly, the time interval partition of the entire scheduling period was optimized by load distribution variation index; secondly, a plurality of time intervals were optimized and reconfigured by using the multi-objective particle swarm optimization model of multi-period encoding; finally, the optimal time interval numbers was determined by gradually approaching the falling threshold of net loss. If the system contained DG, DG’s output curve was determined by the analysis of its dynamic characteristics. The results of the test showed that the proposed method was effective to solve the dynamic reconfiguration with DG.

Optimal Groundwater Management Using Multiobjective Particle Swarm with a New Evolution Strategy

AbstractGroundwater is considered as an important source of freshwater for a variety of purposes including drinking, domestic, industrial, and irrigation uses. Because of increasing population and life standards, there is a growing need for the optimum utilization of groundwater resources. In this paper, a multiobjective particle swarm optimization model with a new evolutionary strategy based on the compromise solution of the Pareto-front optimal solutions is presented. The advantage of this proposed model stems from using a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process. The new evolutionary strategy is verified on a variety of multiobjective standard test problems with either connected or disconnected Pareto fronts. The proposed multiobjective evolutionary strategy is reminiscent of single-objective optimization, in that its fitness assignment and convergence criteria are both based on tracking a single evolving solution over the search history. Details o...

Research on cultural-based multi-objective particle swarm optimization in image compression quality assessment

Quantization is a main factor which affects the performance of the JPEG compression. Compression ratio and the quality of the decoded images are both determined by quantization tables. Appropriate choice of the quantization tables is the key to the Compression performance. To select different optimal quantization tables for different classes of images is multi-objective optimal problem. A cultural-based multi-objective particle swarm optimization model is proposed in this paper. And, different trade-offs between image compression and quality is presented. The simulation result shows that the proposed model is effective to the choice of image compression quality.