A robust ant colony optimization for continuous functions

Chen, Zhiming; Zhou, Shaorui; Luo, Jieting

doi:10.1016/j.eswa.2017.03.036

Cited by 46 publications

(38 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the GWO algorithm also exhibits the problem of easily reaching a local optimal solution, as with any other swarm intelligence algorithm [35][36][37][38]. To address this problem, the GWO algorithm was improved in this study as follows:…”

Section: Improved Gwo Algorithmmentioning

confidence: 99%

“…An optimal simulation was performed to test the optimal ability of the improved GWO algorithm. A single-mode function and multi-mode function were selected for the test [35][36][37][38]. f 1 (x), f 2 (x), and are single-mode functions that represent the typical method of evaluating the global convergence of an algorithm.…”

Section: Verification Of the Superiority Of The Improved Gwo Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm

et al. 2018

View full text Add to dashboard Cite

Featured Application: A bi-level optimal model and relevant algorithm is proposed in the paper to achieve the safe, stable, and economical operation of the distribution network comprising a micro energy grid. The model and algorithm proposed in the paper can provide useful theoretical reference for the integrated energy service company to achieve optimal operation and control of the distribution network comprising a micro energy grid.

show abstract

Section: Improved Gwo Algorithmmentioning

confidence: 99%

Section: Verification Of the Superiority Of The Improved Gwo Algorithmmentioning

confidence: 99%

Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm

et al. 2018

View full text Add to dashboard Cite

show abstract

“…One of the main limitations of the TFP method remains in its difficulty in coping with uncertainties described as probability distributions when the available historical data is sufficient (e.g., pollutant discharge allowances) [26][27][28]. Such a problem can be formulated as a two-stage stochastic programming with a recourse (TSP) model [23].…”

Section: Methodsmentioning

confidence: 99%

A Recourse-Based Type-2 Fuzzy Programming Method for Water Pollution Control under Uncertainty

Liu

Huang

et al. 2017

Symmetry

View full text Add to dashboard Cite

Abstract:In this study, a recourse-based type-2 fuzzy programming (RTFP) method is developed for supporting water pollution control of basin systems under uncertainty. The RTFP method incorporates type-2 fuzzy programming (TFP) within a two-stage stochastic programming with recourse (TSP) framework to handle uncertainties expressed as type-2 fuzzy sets (i.e., a fuzzy set in which the membership function is also fuzzy) and probability distributions, as well as to reflect the trade-offs between conflicting economic benefits and penalties due to violated policies. The RTFP method is then applied to a real case of water pollution control in the Heshui River Basin (a rural area of China), where chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and soil loss are selected as major indicators to identify the water pollution control strategies. Solutions of optimal production plans of economic activities under each probabilistic pollutant discharge allowance level and membership grades are obtained. The results are helpful for the authorities in exploring the trade-off between economic objective and pollutant discharge decision-making based on river water pollution control.

show abstract

“…Thus, the number of all objective functions, their coefficient, constraints, and decision variables is naturally known. At the same time, branch and bound approach as one of optimization algorithms is applied to solve this model [46][47][48][49]. Specifically, the objectives and constraints are mapped into the corresponding elements involved in branch and bound approach.…”

Section: A Case Study From Wenchuan Earthquakementioning

confidence: 99%

Multi-Objective Optimization Model of Emergency Organization Allocation for Sustainable Disaster Supply Chain

Cao

Qin

et al. 2017

Sustainability

View full text Add to dashboard Cite

Abstract:To mitigate or reduce various losses and improve efficiency of disaster response, the focus of this paper is to design optimized strategies of emergency organization allocation regarding sustainability. Firstly, an integrated framework including several elements such as emergency organization, task, decision-agents, environment and their relations is developed from a systematic perspective. Then, this problem is formulated as a novel multi-objective 0-1 integer programming model to minimize total weighted completion times, total carbon emissions and total emergency costs. Next, branch and bound approach and handling strategies for multiple objectives are designed to solve this model. Finally, a case study from the Wenchuan earthquake is presented to illustrate the proposed model and solution strategies. Computational results demonstrate their significant potential advantages on allocating emergency organization from the perspectives of best practice, objective functions, preferences of decision-agents, and problem size.

show abstract

A robust ant colony optimization for continuous functions

Cited by 46 publications

References 20 publications

Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm

Optimal Operation Analysis of the Distribution Network Comprising a Micro Energy Grid Based on an Improved Grey Wolf Optimization Algorithm

A Recourse-Based Type-2 Fuzzy Programming Method for Water Pollution Control under Uncertainty

Multi-Objective Optimization Model of Emergency Organization Allocation for Sustainable Disaster Supply Chain

Contact Info

Product

Resources

About