scholarly journals Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS

2018 ◽  
Vol 7 (8) ◽  
pp. 292 ◽  
Author(s):  
Bahram Saeidian ◽  
Mohammad Mesgari ◽  
Biswajeet Pradhan ◽  
Mostafa Ghodousi
Keyword(s):  
Heuristic Algorithms ◽  
Initial Conditions ◽  
Optimization Algorithms ◽  
Gis Analysis ◽  
Clustering Method ◽  
Geospatial Information ◽  
Location Allocation ◽  
Urban Disaster ◽  
Location Allocation Problem ◽  
Selection Of

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

2017 ◽  
Vol 16 (05) ◽  
pp. 1339-1357 ◽  
Author(s):  
Kun Guo ◽  
Qishan Zhang
Keyword(s):  
Reverse Logistics ◽  
Vehicle Routing Problem ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Routing Problem ◽  
Waste Products ◽  
Location Allocation ◽  
Bee Colony ◽  
Np Complete ◽  
Location Allocation Problem

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals Design and Automated Optimization of an Internal Turret Mooring System in the Frequency and Time Domain

2021 ◽  
Vol 9 (6) ◽  
pp. 581
Author(s):  
Hongrae Park ◽  
Sungjun Jung
Keyword(s):  
System Design ◽  
Time Domain ◽  
Initial Conditions ◽  
Minimum Weight ◽  
Optimization Algorithms ◽  
Local Optimization ◽  
Mooring System ◽  
Floating Offshore Wind Turbines ◽  
Global And Local Optimization ◽  
Global And Local

A cost-effective mooring system design has been emphasized for traditional offshore industry applications and in the design of floating offshore wind turbines. The industry consensus regarding mooring system design is mainly inhibited by previous project experience. The design of the mooring system also requires a significant number of design cycles. To take aim at these challenges, this paper studies the application of an optimization algorithm to the Floating Production Storage and Offloading (FPSO) mooring system design with an internal turret system at deep-water locations. The goal is to minimize mooring system costs by satisfying constraints, and an objective function is defined as the minimum weight of the mooring system. Anchor loads, a floating body offset and mooring line tensions are defined as constraints. In the process of optimization, the mooring system is analyzed in terms of the frequency domain and time domain, and global and local optimization algorithms are also deployed towards reaching the optimum solution. Three cases are studied with the same initial conditions. The global and local optimization algorithms successfully find a feasible mooring system by reducing the mooring system cost by up to 52%.

scholarly journals Healthcare Facility Location-Allocation Optimization for China’s Developing Cities Utilizing a Multi-Objective Decision Support Approach

2018 ◽  
Vol 10 (12) ◽  
pp. 4580 ◽  
Author(s):  
Li Wang ◽  
Huan Shi ◽  
Lu Gan
Keyword(s):  
Heuristic Algorithms ◽  
Programming Model ◽  
Rapid Development ◽  
Supply And Demand ◽  
Healthcare Facility ◽  
Allocation Model ◽  
Location Allocation ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Allocation Problems

With rapid development of the healthcare network, the location-allocation problems of public facilities under increased integration and aggregation needs have been widely researched in China’s developing cites. Since strategic formulation involves multiple conflicting objectives and stakeholders, this paper presents a practicable hierarchical location-allocation model from the perspective of supply and demand to characterize the trade-off between social, economical and environmental factors. Due to the difficulties of rationally describing and the efficient calculation of location-allocation problems as a typical Non-deterministic Polynomial-Hard (NP-hard) problem with uncertainty, there are three crucial challenges for this study: (1) combining continuous location model with discrete potential positions; (2) introducing reasonable multiple conflicting objectives; (3) adapting and modifying appropriate meta-heuristic algorithms. First, we set up a hierarchical programming model, which incorporates four objective functions based on the actual backgrounds. Second, a bi-level multi-objective particle swarm optimization (BLMOPSO) algorithm is designed to deal with the binary location decision and capacity adjustment simultaneously. Finally, a realistic case study contains sixteen patient points with maximum of six open treatment units is tested to validate the availability and applicability of the whole approach. The results demonstrate that the proposed model is suitable to be applied as an extensive planning tool for decision makers (DMs) to generate policies and strategies in healthcare and design other facility projects.

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Sign in / Sign up

Export Citation Format

Share Document