scholarly journals Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

2009 ◽  
Vol 11 (1) ◽  
pp. 79-88 ◽  
Author(s):  
M. Janga Reddy ◽  
D. Nagesh Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Resource Management ◽  
Water Resources ◽  
Water Resource Management ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multi Objective

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Multi-Reservoir Ecological Operation Using Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 641-642 ◽  
pp. 65-69 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Water Resources ◽  
Particle Swarm ◽  
Main Stream ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
River Ecosystem ◽  
Swarm Optimization ◽  
Multi Objective

Traditional reservoir operation ignores ecological demands of rivers. This would probably lead to degradation of river ecosystem. In order to alleviate the influence of reservoirs on river ecosystem, multi-objective reservoir ecological operation was proposed from perspective of maintaining the river ecosystem health. Multi-objective mathematical model of multi-reservoir ecological operation was established. A multi-objective particle swarm optimization (MOPSO) algorithm was introduced to generate a set of Pareto-optimal solutions. In addition, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making method was presented to choose the desired alternative from a set of Pareto-optimal solutions. Finally, the proposed approach was applied to the ecological operation of the reservoirs at the main stream of Xiuhe river in Poyang Lake basin in China. The results show that the proposed approach is able to offer many alternative policies for the water resources managers, and it is a viable alternative to solve multi-objective water resources and hydrology problems.

Comprehensive Optimization Management of Engineering Project Based on Multi-Objective Particle Swarm Optimization

2020 ◽  
Vol 13 ◽  
Author(s):  
Yong Xiang ◽  
Huidan Zheng ◽  
Wuwen Cao ◽  
Dong Gong ◽  
Jiazhen Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Traditional Management ◽  
Multi Objective ◽  
Management Objectives ◽  
Optimization Management

: As the construction industry becomes more sustainable in the future, such as green, ecology, and safety, the higher the requirements for the ultimate objectives of the project.The traditional management objectives of investment, duration, and quality can no longer meet the requirements of comprehensive optimization management. Therefore, from the perspective of the project owners, the work introduced the safety and environmental objectives based on traditional management objectives. The thesis analyzes the relationship between the objectives, and builds the equilibrium optimization model. Moreover, this thesis uses multi-objective particle swarm optimization (MOPSO) to solve the problem, and obtains a series of Pareto optimal solutions. Then, according to the specific requirements of project management and the use of the efficacy coefficient method, the best solution is selected from the Pareto optimal solutions. Finally, a Sichuan wind power project is taken as an example. The work used the MOPSO to run 1,000 trails, and calculate the mean and standard deviation. It verified the rationality of model and the practicability of MOPSO.

scholarly journals The Biobjective Bike-Sharing Rebalancing Problem with Balance Intervals: A Multistart Multiobjective Particle Swarm Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Yongji Jia ◽  
Yuanyuan Xu ◽  
Dong Yang ◽  
Jia Li
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Variable Neighbourhood Search ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Bike Sharing

The bike-sharing system (BSS), as a sustainable way to deal with the “last mile” problem of mass transit systems, is increasingly popular in recent years. Despite its success, the BSS tends to suffer from the mismatch of bike supply and user demand. BSS operators have to transfer bikes from surplus stations to deficit stations to redistribute them among stations by means of trucks. In this paper, we deal with the bike-sharing rebalancing problem with balance intervals (BRP-BIs), which is a variant of the static bike-sharing rebalancing problem. In this problem, the equilibrium of station is characterized by a balance interval instead of a balance point in the literature. We formulate the BRP-BI as a biobjective mixed-integer programming model with the aim of determining both the minimum cost route for a single capacitated vehicle and the maximum average rebalance utility, an index for the balanced degree of station. Then, a multistart multiobjective particle swarm optimization (MS-MOPSO) algorithm is proposed to solve the model such that the Pareto optimal solutions can be derived. The proposed algorithm is extended with crossover operator and variable neighbourhood search to enhance its exploratory capability. Compared with Hybrid NSGA-II and MOPSO, the computational experimental results demonstrate that our MS-MOPSO can obtain Pareto optimal solutions with higher quality.

scholarly journals An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Tao Zhang ◽  
Tiesong Hu ◽  
Yue Zheng ◽  
Xuning Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Multiobjective Programming Problem

An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

scholarly journals Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Xiaoshu Zhu ◽  
Jie Zhang ◽  
Junhong Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Uniform Design ◽  
Particle Swarm ◽  
Test Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

2012 ◽  
pp. 1-16
Author(s):  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung Bae Cho
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Discrete Values ◽  
Continuous Domain

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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