In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Experimental And Quasi Experimental Designs For Research

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers' opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers' performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers' assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.

/pdf/sustainable-supplier-selection-a-ranking-model-based-on-1eyl039fne.pdf

Sustainable supplier selection: A ranking model based on fuzzy inference system

A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: An automotive case

Within supply chains activities, selecting appropriate suppliers based on the sustainability criteria (economic, environmental and social) can help companies move toward sustainable development. Although several studies have recently been accomplished to incorporate sustainability criteria into supplier selection problem, much less attention has been devoted to developing a comprehensive mathematical model that allocates the optimal quantities of orders to suppliers considering lot-sizing problems. In this research, we propose an integrated approach of rule-based weighted fuzzy method, fuzzy analytical hierarchy process and multi-objective mathematical programming for sustainable supplier selection and order allocation combined with multi-period multi-product lot-sizing problem. The mathematical programming model consists of four objective functions which are minimising total cost, maximising total social score, maximising total environmental score and maximising total economic qualitative score. The prop...

Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process

This paper present a comparison between Fuzzy Cognitive Map (FCM) learning approaches and algorithms. FCMs are fuzzy digraphs with weights and feedback loops, consisting of nodes interconnected through directed arcs mostly used for knowledge representation and system modelling. One of the most important characteristics of FCMs is their learning capabilities. FCMs are normally constructed through experts’ opinions, thus they maybe subjective. Learning algorithms are introduced to overcome this inconvenient. One of the main problem of the new proposed algorithms is their validation. Using theoretical and experimental analysis, this research aims to (1) compare FCM learning algorithms proposed in the literature, (2) provide a validation tool for new FCM learning algorithms (3) present a new algorithm called Asexual Reproduction Optimisation (ARO) with one of its extensions – Modified ARO (MARO) – as a novel FCM learning algorithm to use the validation tool proposed. According to the findings from the literature, it seems that among FCM learning approaches, population-based algorithms perform better compared to other algorithms. Also, the testing was done in five benchmark datasets and a synthetic dataset with different node sizes using two criteria of in-sample and out-of-sample errors. The results show that MARO outperforms other algorithms in both error functions in terms accuracy and robustness.

Learning Fuzzy Cognitive Maps with modified asexual reproduction optimisation algorithm

This article reviews the use of case study research for both practical and theoretical issues especially in management field with the emphasis on management of technology and innovation. Many resea...

https://journals.sagepub.com/doi/pdf/10.1177/1609406918817954

Toward Developing a Framework for Conducting Case Study Research

This paper proposes a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as Asexual Reproduction Optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, each individual produces an offspring called bud through a reproduction mechanism; thereafter parent and its offspring compete according to a performance index obtained from the underlying objective function of the given optimization problem. This process leads to the fitter individual. ARO's adaptive search ability and its strong and weak points are described in this paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of Particle Swarm Optimization (PSO). Results of simulation illustrate that ARO remarkably outperforms PSO.

ARO: A new model-free optimization algorithm inspired from asexual reproduction

Billions of people are affected by worldwide natural and man-made disasters each year There is considerable evidence that the number of disasters is increasing, but there is still relatively little-published work aimed at improving the understanding of the different barriers of the humanitarian supply chain (HSC) and finding suitable solutions for dealing with them The aim of this paper is to provide a comprehensive review of HSC barriers in the context of a case study of the Tehran Red Crescent Societies An integrated approach using Fuzzy Delphi and the Best-Worst method (BWM) has been used for analyzing the barriers Based on the literature and interviews with experts, 28 barriers of the humanitarian supply chain were identified According to the Fuzzy Delphi result, 22 barriers were assessed that subsequently classified into 9 categories After that, the BWM method calculated the importance of each barrier The findings showed that Cultural, Managerial, and Educational barriers are the important barriers This research provides useful guidelines for policy makers so that they can benefit from the results to optimize their solutions

Analyzing the barriers to humanitarian supply chain management: A case study of the Tehran Red Crescent Societies

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.

https://isiarticles.com/bundles/Article/pre/pdf/19155.pdf

Mohammad Reza Sadeghi Moghadam

Papers

Learning Fuzzy Cognitive Maps with modified asexual reproduction optimisation algorithm

Toward Developing a Framework for Conducting Case Study Research

ARO: A new model-free optimization algorithm inspired from asexual reproduction

Analyzing the barriers to humanitarian supply chain management: A case study of the Tehran Red Crescent Societies

Inventory lot-sizing with supplier selection using hybrid intelligent algorithm