Performance Of Multi-objective Genetic Algorithm Research Articles

Balancing assembly lines operating with heterogeneous workers under uncertainty in task processing times

PurposeThis paper considers the assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times. This problem is an extension of the (simple) SALBP-2 in which task times are worker-dependent and concurrently uncertain. Two criteria are simultaneously considered for minimization, namely, fuzzy cycle time and fuzzy smoothness index.Design/methodology/approachFirst, we show how fuzzy concepts can be used for managing uncertain task times. Then, we present a multiobjective genetic algorithm (MOGA) to solve the problem. MOGA is devoted to the search for Pareto-optimal solutions. For facilitating effective trade-off decision-making, two different MO approaches are implemented and tested within MOGA: a weighted-sum based approach and a Pareto-based approach.FindingsExperiments over a set of fuzzified test problems show the effect of these approaches on the performance of MOGA while verifying its efficiency in terms of both solution and time quality.Originality/valueTo the author’s knowledge, no previous published work in the literature has studied the biobjective assembly line worker assignment and balancing problem of type-2 (ALWABP-2) with fuzzy task times.

Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to a “numerical” population of solutions to optimize a fitness function. GA implementations must find a compromise between the breath of the search (to avoid being trapped into local minima) and its depth (to prevent a rough approximation of the optimal solution). Most algorithms use “elitism”, which allows preserving some of the current best solutions in the successive generations. If the initial population is randomly selected, as in many GA packages, the elite may concentrate in a limited part of the Pareto frontier preventing its complete spanning. A full view of the frontier is possible if one, first, solves the single-objective problems that correspond to the extremes of the Pareto boundary, and then uses such solutions as elite members of the initial population. The paper compares this approach with more conventional initializations by using some classical tests with a variable number of objectives and known analytical solutions. Then we show the results of the proposed algorithm in the optimization of a real-world system, contrasting its performances with those of standard packages.

Neural network-based multiobjective optimization algorithm for nonlinear beam dynamics

Multiobjective genetic algorithms (MOGAs) have proven to be powerful in solving multiobjective problems in the accelerator field. Nevertheless, for explorative problems that have many variables and local optima, the performance of MOGAs is not always satisfactory, especially when a small population size is used due to practical limitations, e.g., limited computing resources. To deal with this challenge, in this paper an enhanced MOGA, neural network-based MOGA (NBMOGA), is proposed. In this method, the data produced with the standard MOGA are used to train a neural network. The neural network is fast to produce a large pool of objective function estimates, with sufficiently high accuracy. A subset of the most competitive estimates is selected to form a population (matching MOGA population size), which is then evaluated with the MOGA evaluator. By taking three classic multiobjective problems as examples, we demonstrate that the proposed method promises a faster convergence and a higher degree of diversity than that available with the standard MOGA and other three optimization methods that have been applied in the accelerator field, i.e., the multiobjective particle swarm optimization (MOPSO), the combination of MOPSO and MOGA, and the clustering enhanced MOGA. And then this method is applied to a time-consuming optimization problem, the dynamic aperture and Touschek lifetime optimization of the high energy photon source. It turns out that, within the same optimization time, a better set of solutions in the objective space can be obtained with the NBMOGA than using other methods. The Touschek lifetime can be improved by about 10% compared with using the standard MOGA, with approximately the same dynamic aperture area. Besides, a higher degree of diversity among solutions is observed with the NBMOGA than using other tested methods.

Uncertain programming models for multi-objective shortest path problem with uncertain parameters

The shortest path problem is considered as one of the essential problems in network optimization with a wide range of real-world applications. Modelling such real-world applications involves various indeterminate phenomena which can be estimated through human beliefs. The uncertainty theory proposed by Liu (Uncertain theory, 2nd edn., Springer, Berlin, 2007) is widely regarded as a legitimate tool to deal with such uncertainty. This paper presents an uncertain multi-objective shortest path problem (UMSPP) for a weighted connected directed graph (WCDG), where every edge weight is associated with two uncertain parameters: cost and time. These parameters are represented as uncertain variables. Here, we have formulated the expected value model and chance-constrained model of the proposed UMSPP, and the corresponding deterministic transformation of these models is also presented. Subsequently, the deterministic models are solved with a classical multi-objective solution method, namely the global criterion method. Furthermore, two multi-objective genetic algorithms (MOGAs): nondominated sorting genetic algorithm II (NSGA-II) and multi-objective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation (MOCHC), are employed to solve these models. A suitable example is provided to illustrate the proposed model. Finally, the performance of MOGAs is compared for five randomly generated instances of UMSPP.

Uncertain multi-objective Chinese postman problem

Chinese postman problem is one of the significant combinatorial optimization problems with a wide range of real-world applications. Modelling such real-world applications quite often needs to consider some uncertain factors for which the belief degrees of the experts are essential. Liu (Uncertainty Theory, 2nd edn. Springer, Berlin, 2007) proposed uncertainty theory to model such human beliefs. This paper presents a multi-objective Chinese postman problem under the framework of uncertainty theory. The objectives of the problem are to maximize the total profit earned and to minimize the total travel time of the tour of a postman. Here, we have proposed an expected value model (EVM) for the uncertain multi-objective Chinese postman problem (UMCPP). The deterministic transformation of the corresponding EVM is done by computing the expected value of the uncertain variable using 999-method for which we have proposed an algorithm, 999-expected value model-uncertain multi-objective Chinese postman problem. Subsequently, the model is solved by two classical multi-objective solution techniques, namely global criterion method and fuzzy programming method. Two multi-objective genetic algorithms (MOGAs): nondominated sorting genetic algorithm II and multi-objective cross-generational elitist selection, heterogeneous recombination and cataclysmic mutation are also used to solve the model. A numerical example is presented to illustrate the proposed model. Finally, the performance of MOGAs is compared on six randomly generated instances of UMCPP.

Multi‐objective approach of energy efficient workflow scheduling in cloud environments

SummaryScheduling the tasks of a workflow to the cloud resources is a well‐known N‐P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi‐Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources. We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi‐objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.

Energy consumption enhancement and environmental life cycle assessment in paddy production using optimization techniques

In recent years, environmental impacts of energy consumption in different production systems have become more adverse. Agricultural systems, as results of food security and production growth, are nowadays significant energy consumers. Owing to various reasons, energy consumption in these systems, especially those in developing countries, are often inefficient. As such, there are three objectives in this study. The first objective is the assessment of energy flow, cumulative exergy demand and life cycle of paddy fields. The second goal is the optimization of energy consumption by employing Data Envelopment Analysis (DEA) and Multi-Objective Genetic Algorithm (MOGA) for enhancement of energy efficiency (reduction in energy consumption and increase performance in paddy production) and reduction of adverse environmental effects in paddy production. The third objective is the provision of the optimum pattern of energy consumption in paddy production in Guilan Province of Iran. Optimization results indicate that energy savings by employing DEA and MOGA are 21.15% and 71.63%, respectively. They indicate that the enhancement of the efficiency of paddy production is mainly in terms of toxins and chemical fertilizers. Besides, all adverse environmental impacts by MOGA are significantly less than from its counterparts by DEA and present farms. The most significant difference between MOGA and DEA is associated to ozone layer depletion. As a result, MOGA can be introduced as an adequate optimization method to determine the best mix of paddy production inputs, minimizing adverse environmental impact and maximizing performances of farms and energy efficiency simultaneously. This study highlights the utilization and the performance of MOGA in paddy production systems to determine the optimized composition for mixing energy, yield and environmental impacts. The implementation of the recommended consumption patterns will be able to attain sustainable development in paddy production in Guilan province of Iran, owing to the protection of non-renewable resources, the reduction of adverse environmental effects and the improvement of economic conditions for rice farmers.

Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran

Management of energy use and reduction of greenhouse gas emissions (GHG) in agricultural system is the important topic. For this purpose, many methods have been proposed in different researches for solution of these items in recent years. Obviously, the selection of appropriate method was a new concern for researchers. Accordingly, the energy inputs and GHG emissions of orange production in north of Iran were modeled and optimized by artificial neural networks (ANN) and multi-objective genetic algorithm (MOGA) in this study and the results obtained were compared with the results of data envelopment analysis (DEA) approach. Results showed that, on average, an amount of 25,582.50 MJ ha−1 was consumed in orange orchards in the region and the nitrogen fertilizer was accounted for 36.84 % of the total input energy. The outcomes of this study demonstrated that on average 803 kg carbon dioxide (kgCO2eq.) is emitted per ha and diesel fuel is responsible for 35.7 % of all emissions. The results of ANN signified that they were capable of modeling crop output and total GHG emissions where the model with a 13-4-2 topology had the highest accuracy in both training and testing steps. The optimization of energy consumption using MOGA revealed that the total energy consumption and GHG emissions of orange production can be reduced to the values of 13,519 MJ ha−1 and 261 kgCO2eq. ha−1, respectively. A comparison between MOGA and DEA clearly showed the better performance of MOGA due to simultaneous application of different objectives and the global optimum solutions produced by the last generation.

Multi-Objective Genetic Algorithm with Strategies for Dying of Solution

Genetic Algorithm (GA) mimics natural evolutionary process. Since dying of an organism is important part of natural evolutionary process, GA should have some mechanism for dying of solutions just like GA have crossover operator for birth of solutions. In nature, occurrence of event of dying of an organism has some reasons like aging, disease, malnutrition and so on. In this work we propose three strategies of dying or removal of solution from next generation population. Multi-objective Genetic Algorithm (MOGA) takes decision of removal of solution, based on one of these three strategies. Experiments were performed to show impact of dying of solutions and dying strategies on the performance of MOGA.