Ant colony optimization for real-world vehicle routing problems

Ant colony optimization (ACO) is a metaheuristic for combinatorial optimization problems. In this paper we report on its successful application to the vehicle routing problem (VRP). First, we introduce the VRP and some of its variants, such as the VRP with time windows, the time dependent VRP, the VRP with pickup and delivery, and the dynamic VRP. These variants have been formulated in order to bring the VRP closer to the kind of situations encountered in the real-world. Then, we introduce the basic principles of ant colony optimization, and we briefly present its application to the solution of the VRP and of its variants.     

Simulated annealing for the vehicle routing problem with two-dimensional loading constraints

This paper addresses the capacitated vehicle routing problem with two-dimensional loading constraints (2L-CVRP). The 2L-CVRP is a combination of the two most important problems in distribution logistics, which are loading of freight into vehicles, and the successive routing of the vehicles to satisfy customer demand. The objective is to minimize the transportation cost. All vehicles must start and terminate at a central depot, and the transported items carried by the vehicles must be feasibly packed into the loading surfaces of the vehicles. A simulated annealing algorithm to solve the problem is presented, in which the loading component of the problem is solved through a collection of packing heuristics. A novel approach to plan packing is employed. An efficient data structure (Trie) is used to accelerate the algorithm. The extensive computational results prove the effectiveness of the algorithm.     

Genetic algorithm for multi-objective experimental optimization

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).     

Integrative data analysis for functional prediction: a multi-objective optimization approach

SUMMARY: An integrative classification system for functional genomics is introduced. A comparison with a previous study of the yeast mitochondrial proteome is presented. AVAILABILITY: A demonstration prototype, interSearch, is available on request. SUPPLEMENTARY INFORMATION: http://ijsr32.infj.ulster.ac.uk/~e10110731/interSearch.     

Evolutionary algorithms for solving multi-objective travelling salesman problem

This paper studies the application of evolutionary algorithms for bi-objective travelling salesman problem. Two evolutionary algorithms, including estimation of distribution algorithm (EDA) and genetic algorithm (GA), are considered. The solution to this problem is a set of trade-off alternatives. The problem is solved by optimizing the order of the cities so as to simultaneously minimize the two objectives of travelling distance and travelling cost incurred by the travelling salesman. In this paper, binary-representation-based evolutionary algorithms are replaced with an integer-representation. Three existing EDAs are altered to use this integer-representation, namely restricted Boltzmann machine (RBM), univariate marginal distribution algorithm (UMDA), and population-based incremental learning (PBIL). Each city is associated with a representative integer, and the probability of any of this representative integer to be located in any position of the chromosome is constructed through the modeling approach of the EDAs. New sequences of cities are obtained by sampling from the probabilistic model. A refinement operator and a local search operator are proposed in this piece of work. The EDAs are subsequently hybridized with GA in order to complement the limitations of both algorithms. The effect that each of these operators has on the quality of the solutions are investigated. Empirical results show that the hybrid algorithms are capable of finding a set of good trade-off solutions.     

Pareto-based multi-objective optimization for classification in data mining

This paper explores the possibility of classification based on Pareto multi-objective optimization. The efforts on solving optimization problems using the Pareto-based MOO methodology have gained increasing impetus on comparison of selected constraints. Moreover we have different types of classification problem based on optimization model like single objective optimization, MOO, Pareto optimization and convex optimization. All above techniques fail to generate distinguished class/subclass from existing class based on sensitive data. However, in this regard Pareto-based MOO approach is more powerful and effective in addressing various data mining tasks such as clustering, feature selection, classification, and knowledge extraction. The primary contribution of this paper is to solve such noble classification problem. Our work provides an overview of the existing research on MOO and contribution of Pareto based MOO focusing on classification. Particularly, the entire work deals with association of sub-features for noble classification. Moreover potentially interesting sub-features in MOO for classification are used to strengthen the concept of Pareto based MOO. Experiment has been carried out to validate the theory with different real world data sets which are more sensitive in nature. Finally, experimental results provide effectiveness of the proposed method using sensitive data.     

Local adaptation to biocontrol agents: A multi-objective data-driven optimization model for the evolution of resistance

Spatial and temporal variability in the application of biological control agents such as parasites or pathogenic bacteria can cause the evolution of resistance in pest organisms. Because biocontrol will be more effective if organisms are not resistant, it is desirable to examine the evolution of resistance under different application strategies.We present a computational method that integrates a genetic algorithm with experimental data for predicting when local populations are likely to evolve resistance to biocontrol pathogens. The model incorporates parameters that can be varied as part of pest control measures such as the distribution and severity of the biocontrol agent (e.g., pathogenic fungi). The model predicts the evolution of pathogen defense as well as indirect selection on several aspects of the organism's genetic system. Our results show that both variability of selection within populations as well as mean differences among populations are important in the evolution of defenses against biocontrol pathogens. The mean defense is changed through the pest organism's genotype and the variance is affected by components of the genetic system, namely, the resiliency, recombination rate and number of genes.The data-driven model incorporates experimental data on pathogen susceptibility and the cost of defense. The results suggest that spatial variability rather than uniform application of biological control will limit the evolution of resistance in pest organisms.     

Biotechnological solutions to the nitrogen problem

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An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms.     

A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling

Cluster Computing - Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud...     

Targeted proteomics for metabolic pathway optimization: application to terpene production

Successful metabolic engineering relies on methodologies that aid assembly and optimization of novel pathways in microbes. Many different factors may contribute to pathway performance, and problems due to mRNA abundance, protein abundance, or enzymatic activity may not be evident by monitoring product titers. To this end, synthetic biologists and metabolic engineers utilize a variety of analytical methods to identify the parts of the pathway that limit production. In this study, targeted proteomics, via selected-reaction monitoring (SRM) mass spectrometry, was used to measure protein levels in Escherichia coli strains engineered to produce the sesquiterpene, amorpha-4,11-diene. From this analysis, two mevalonate pathway proteins, mevalonate kinase (MK) and phosphomevalonate kinase (PMK) from Saccharomyces cerevisiae, were identified as potential bottlenecks. Codon-optimization of the genes encoding MK and PMK and expression from a stronger promoter led to significantly improved MK and PMK protein levels and over three-fold improved final amorpha-4,11-diene titer (>500 mg/L).     

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

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A fuzzy multi-objective optimization model for sustainable reverse logistics network design

Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order to deal with uncertain parameters, fuzzy mathematical programming is used, and to obtain solutions on Pareto front, a customized multi-objective particle swarm optimization (MOPSO) algorithm is applied. The validity of the proposed solution procedure has been analyzed in small and large size test problems based on four comparison metrics and computational time using analysis of variance. Finally, in order to indicate the applicability of the suggested model and the practicality of the proposed solution procedure, the model has been implemented in a medical syringe recycling system. The results reveal that the suggested MOPSO algorithm overtakes epsilon-constraint method from the aspects of quality of the solutions as well as computational time. Proper use of the proposed process could help managers efficiently manage the flow of recycled products with regard to environmental and social considerations, and the process offers a sustainable competitive advantage to corporations.     

Biocatalytic solutions to cyclomethicones problem in cosmetics

Silicones are polymers that have properties of great interest for cosmetic and personal care industry, especially D₄ (octamethylcyclotetrasiloxane) and D₅ (decamethylcyclopentasiloxane), generically named cyclomethicones. However, different studies show the hypothesis that the use of these products is harmful and, therefore, several countries have adopted legislative actions to limit their use in personal care and cosmetic products. Faced with this new situation, different cosmetic companies began to look for different alternatives to cyclomethicones, mainly: organomodified silicones or esters. The former are based on the addition of functional groups to the main chain of silicon and oxygen. The latter represent a totally different line, since their functional group is the ester, but they have properties similar to silicones. Esters, especially branched‐chain ones are currently the more promising alternative to replace cyclomethicones in cosmetic formulations. Nowadays, most of them are obtained by chemical reactions that require high temperatures and non‐selective /contaminant catalysts that lead to low‐quality final products. As an alternative, biocatalytic synthesis occurs always at mild operation conditions supplying ultra‐pure, odor, and colorless products with less wastes and side reactions. Therefore, biocatalysis is a valid and environmentally sustainable option for the synthesis of silicone‐substitute esters used in cosmetic formulations.