Hybrid Multi-objective Evolutionary Algorithm Research Articles

Multi-Objective Optimization of the Hydrodynamic Performance of the Second Stage of a Multi-Phase Pump

Most multi-phase pumps used in crude oil production have been developed to satisfy certain pressure specifications. In the design of these pumps, the flow characteristics of the posterior stage are different from those of the prior stage. For this reason, the design of the second stage needs to be supplemented. To optimize performance in this stage, multi-objective optimization to simultaneously increase pressure and efficiency is reported in this article. Flow analyses of the single and multiple phases of the multi-phase pump were conducted by solving three-dimensional steady Reynolds-averaged Navier–Stokes equations. For the numerical optimization, two design variables related to the blade inlet angle were selected. The impeller and the diffuser blades were optimized using a systematic optimization technique combined with a central composite method and a hybrid multi-objective evolutionary algorithm coupled with a surrogate model. The selected optimal model yielded better hydrodynamic performance than the base model, and reasons for this are investigated through internal flow field analysis.

A hybrid multi-objective evolutionary algorithm approach for handling sequence- and machine-dependent set-up times in unrelated parallel machine scheduling problem

This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time, the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on non-zero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs. The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/near-optimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multi-objective algorithm.

A Hybrid Multi-Objective Evolutionary Algorithm for Operating Room Assignment Problem

A Hybrid Multi-Objective Evolutionary Algorithm for Operating Room Assignment Problem

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

ABSTRACTConstrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.

Mean-VaR portfolio optimization: A nonparametric approach

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean–variance model in which the variance is replaced with an industry standard risk measure, Value-at-Risk (VaR), in order to better assess market risk exposure associated with financial and commodity asset price fluctuations. Realistic portfolio optimization in the mean-VaR framework is a challenging problem since it leads to a non-convex NP-hard problem which is computationally intractable. In this work, an efficient learning-guided hybrid multi-objective evolutionary algorithm (MODE-GL) is proposed to solve mean-VaR portfolio optimization problems with real-world constraints such as cardinality, quantity, pre-assignment, round-lot and class constraints. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote efficient convergence by guiding the evolutionary search towards promising regions of the search space. The proposed algorithm is compared with the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2). Experimental results using historical daily financial market data from S & P 100 and S & P 500 indices are presented. The results show that MODE-GL outperforms two existing techniques for this important class of portfolio investment problems in terms of solution quality and computational time. The results highlight that the proposed algorithm is able to solve the complex portfolio optimization without simplifications while obtaining good solutions in reasonable time and has significant potential for use in practice.

Multi-objective inverse scheduling optimization of single-machine shop system with uncertain due-dates and processing times

Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain.

A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems

In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.

Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms

This paper proposes a novel multi-objective hybrid evolutionary algorithm (MOHEA) that allows spatially-explicit modelling of local outbreaks and dispersal of population density. The MOHEA was tested for modelling at once two cyanobacteria populations at one lake site, same population in two different lakes and same population at three different sites of one lake. All experiments with MOHEA utilized water quality time-series and abundances of Anabaena and Cylindrospermopsis monitored in the sub-tropical Lakes Wivenhoe and Somerset in Queensland (Australia) from 1999 to 2010. Results have demonstrated the capacity of MOHEA to determine generic rules that: (1) reveal crucial thresholds for outbreaks of cyanobacteria blooms, and (2) perform spatially-explicit forecasting of timing and magnitudes 7-day-ahead of bloom events.

A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

Preventive maintenance optimization using a Hybrid Multi-Objective Evolutionary Algorithm

This paper is focused on the problem of preventive maintenance optimization in a manufacturing environment, to determine the optimal preventive maintenance frequencies for equipment under cost and profit criteria, considering production, quality and maintenance aspects. The paper is based on a previously developed maintenance model, to execute a benefit and cost optimization process using a Hybrid Multi-Objective Evolutionary Algorithm (Hybrid MOEA) that combines a global search method with a local one. The hybrid algorithm combines the capabilities of both worlds, using a global search technique to effectively explore wide parameter spaces, deal properly with function non-linearities and avoid falling into local optimal solutions, and combining it with the capacities of local search methods to efficiently converge into local optimal solutions. The hybridization is done according to two different schemes. Firstly, ‘a posteriori’ scheme has been implemented, where the MOEA runs for a number of generations obtaining an approximation of the Pareto front to apply then a local search from each non-dominated solution of the front. Secondly, an ‘on-line’ scheme has been developed, where in each generation (or after a reduced number of generations) of the evolutionary algorithm a local search is applied on each non-dominated solution to return then the improved solutions to the MOEA as the current population. Both hybrid schemes have been applied to an industrial manufacturing case where the benefit of implementing the hybrid optimization approach is shown, by comparing the hybrid schemes with the MOEA.

A Dimensional Diversity Based Hybrid Multiobjective Evolutionary Algorithm for Optimization Problem

Multiobjective density driven evolutionary algorithm (MODdEA) has been quite successful in solving multiobjective optimization problems (MOPs). To further improve its performance and address its deficiencies, this paper proposes a hybrid evolutionary algorithm based on dimensional diversity (DD) and firework explosion (FE). DD is defined to reflect the diversity degree of population dimension. Based on DD, a selection scheme is designed to balance diversity and convergence. A hybrid variation based on FE and genetic operator is designed to facilitate diversity of population. The proposed algorithm is tested on 14 tests problems with diverse characteristics and compared with three state-of-the-art designs. Experimental results show that the proposed design is better or at par with the chosen state-of-the-art algorithms for multiobjective optimization.

Dimensional optimization of a micro-channel heat sink using Jaya algorithm

In this paper, a recently proposed optimization algorithm named as ‘Jaya algorithm’ is used for the dimensional optimization of a micro-channel heat sink. Two case studies are considered and in both the case studies two objective functions related to the heat transfer and pressure drop, i.e. thermal resistance and pumping power, are formulated to examine the performance of the micro-channel heat sink. In the first case study, two non-dimensional design variables related to the micro-channel depth, width and fin width are chosen and their ranges are decided through preliminary calculations of three-dimensional Navier–Stokes and energy equations. In the second case study, three design variables related to the width of the micro-channel at the top and bottom, depth of the micro-channel and width of fin are considered. The objective functions in both the case studies are expressed in terms of the respective design variables and the two objectives are optimized simultaneously using the proposed Jaya algorithm. The results are compared with the results obtained by the TLBO algorithm and a hybrid multi-objective evolutionary algorithm (MOEA) and numerical analysis. The results obtained by the application of Jaya algorithm are found much better than those reported by the other approaches.

Optimization of a regenerative blower to enhance aerodynamic and aeroacoustic performance

A multi-objective optimization was performed to simultaneously enhance the aerodynamic and aeroacoustic performance of a side channel-type regenerative blower. The aerodynamic characteristics of the regenerative blower were investigated numerically using threedimensional steady and unsteady Reynolds-averaged Navier-Stokes analysis with a shear stress transport turbulence model. On the basis of the aerodynamic sources extracted from the unsteady flow calculation, three-dimensional aeroacoustic analysis was implemented using a finite/infinite element method by solving the variational formulation of Lighthill’s analogy. Three design variables, viz., the ratio of blade height to impeller diameter, ratio of blade width to impeller diameter, and angle between inlet and outlet port, were selected as design variables, and the efficiency and sound pressure level at the design point of the blower were selected as objective functions for the optimization. These two objective functions were estimated numerically through three-dimensional aerodynamic and aeroacoustic analyses at the design points sampled by Latin hypercube sampling in the design space. Pareto-optimal solutions were obtained using a hybrid multi-objective evolutionary algorithm coupled with radial basis neural network models as surrogates of the objective functions. Three arbitrarily selected optimum designs of the regenerative blower show significant increases in efficiency and reductions in sound pressure level compared to a reference design.

Hybrid multiobjective artificial bee colony for multiple sequence alignment

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.

Aerodynamic and aeroacoustic optimization for design of a forward-curved blades centrifugal fan

This paper presents a multidisciplinary optimization procedure for enhancing the aerodynamic and aeroacoustic performance of a forward-curved blade centrifugal fan for residential ventilation. Flow analysis in a forward-curved blade centrifugal fan was conducted by solving three-dimensional steady and unsteady Reynolds-averaged Navier–Stokes equations using the shear stress transport turbulence model. On the basis of the aerodynamic sources extracted from the unsteady flow, aeroacoustic analysis was implemented in a finite/infinite element method by solving the variational formulation of Lighthill’s analogy. Experiments were performed to obtain aerodynamic and aeroacoustic measurements for validation of numerical results. The single- and multi-objective optimizations were performed sequentially. The single-objective optimization was carried out to improve the efficiency of the fan using a radial basis neural network surrogate model with four design variables defining the scroll cut-off angle, scroll diffuser expansion angle, diameter ratio of the impeller, and blade exit angle. Multi-objective optimization based on the single-objective optimization result was carried out to simultaneously improve the efficiency and reduce the sound pressure through a hybrid multi-objective evolutionary algorithm coupled with a response surface approximation surrogate model with two design variables defining the scroll cut-off radius and distance. These objective functions were accessed numerically through three-dimensional aerodynamic and aeroacoustic analyses at the design points sampled by Latin hypercube sampling in the design space. Arbitrary selected optimum designs in the Pareto-optimal solutions yielded significant increases in efficiency and decreases in the sound pressure level compared to the reference design.

Multi-objective robust optimisation of unidirectional carbon/glass fibre reinforced hybrid composites under flexural loading

A multi-objective robust optimisation (MORO) of carbon and glass fibre-reinforced hybrid composites under flexural loading based on an a posteriori approach has been presented in this paper. The hybrid composite comprised of T700S carbon/epoxy laminate at the tensile side and E glass/epoxy laminate at the compressive side. The conflicting objectives for optimisation were to minimise the cost and weight of the composite subject to the constraint of a minimum specified flexural strength. Fibre angles and thicknesses of each lamina were considered as uncertain but bounded variables with the worst-case analyses being performed as a non-probabilistic method and the effect of uncertainties being determined. A hybrid multi-objective optimisation evolutionary algorithm (MOEA) was introduced through modification of an elitist non-dominated sorting genetic algorithm (NSGA-II) and combining it with the fractional factorial design method. The performance of the hybrid algorithm was found to be superior to that of the original version of NSGA-II. The multi-objective robust optimisation of the hybrid composite was solved by utilising the proposed algorithm for several levels of strength with the robust Pareto optimal sets being generated and compared. Three scenarios have been considered to illustrate the applicability of the obtained solutions in an a posteriori decision making process.

A hybrid evolutionary algorithm for multiobjective variation tolerant logic mapping on nanoscale crossbar architectures

Nanoscale crossbar architectures have received steadily growing interests as a result of their great potential to be main building blocks in nanoelectronic circuits. However, due to the extremely small size of nanodevices and the bottom-up self-assembly nanofabrication process, considerable process variation will be an inherent vice for crossbar nanoarchitectures. In this paper, the variation tolerant logical mapping problem is treated as a bilevel multiobjective optimization problem. Since variation mapping is an NP-complete problem, a hybrid multiobjective evolutionary algorithm is designed to solve the problem adhering to a bilevel optimization framework. The lower level optimization problem, most frequently tackled, is modeled as the min–max-weight and min-weight-gap bipartite matching (MMBM) problem, and a Hungarian-based linear programming (HLP) method is proposed to solve MMBM in polynomial time. The upper level optimization problem is solved by evolutionary multiobjective optimization algorithms, where a greedy reassignment local search operator, capable of exploiting the domain knowledge and information from problem instances, is introduced to improve the efficiency of the algorithm. The numerical experiment results show the effectiveness and efficiency of proposed techniques for the variation tolerant logical mapping problem.

An External Archive Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Combinatorial Optimization

Domination-based sorting and decomposition are two basic strategies used in multiobjective evolutionary optimization. This paper proposes a hybrid multiobjective evolutionary algorithm integrating these two different strategies for combinatorial optimization problems with two or three objectives. The proposed algorithm works with an internal (working) population and an external archive. It uses a decomposition-based strategy for evolving its working population and uses a domination-based sorting for maintaining the external archive. Information extracted from the external archive is used to decide which search regions should be searched at each generation. In such a way, the domination-based sorting and the decomposition strategy can complement each other. In our experimental studies, the proposed algorithm is compared with a domination-based approach, a decomposition-based one, and one of its enhanced variants on two well-known multiobjective combinatorial optimization problems. Experimental results show that our proposed algorithm outperforms other approaches. The effects of the external archive in the proposed algorithm are also investigated and discussed.

An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems

Generally, in handling traditional scheduling problems, ideal manufacturing system environments are assumed before determining effective scheduling. Unfortunately, “ideal environments” are not always possible. Real systems often encounter some uncertainties which will change the status of manufacturing systems. These may cause the original schedule to no longer to be optimal or even feasible. Traditional scheduling methods are not effective in coping with these cases. Therefore, a new scheduling strategy called “inverse scheduling” has been proposed to handle these problems. To the best of our knowledge, this research is the first to provide a comprehensive mathematical model for multi-objective permutation flow-shop inverse scheduling problem (PFISP). In this paper, first, a PFISP mathematical model is devised and an effective hybrid multi-objective evolutionary algorithm is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In the proposed algorithm, we take an insert method NEH-based (Nawaz–Enscore–Ham) as a local improving procedure and propose several adaptations including efficient initialization, decimal system encoding, elitism and population diversity. Finally, 119 public problem instances with different scales and statistical performance comparisons are provided for the proposed algorithm. The results show that the proposed algorithm performs better than the traditional multi-objective evolution algorithm (MOEA) in terms of searching quality, diversity level and efficiency. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve PFISP in inverse scheduling domain.

Open Energy Market Strategies in Microgrids: A Stackelberg Game Approach Based on a Hybrid Multiobjective Evolutionary Algorithm

The emergence of microsources holds promise to reduce the carbon emissions and exploit more renewables in order to meet the worldwide growing electrical energy demands. However, there exist several challenges, such as optimizing the tradeoff between the use of renewable and nonrenewable energy sources, to leverage affordable electric power while minimizing carbon emissions. Game theoretic approaches have been widely used in various scientific domains and have recently also increasingly been used in smart grids, whereby evolutionary paradigms have been widely deployed as a popular heuristic search method to solve and optimize complex real-life scientific problems. A promising approach is the development of such evolutionary algorithms and game theoretic approaches in the context of open energy markets. In this paper, we develop an analytic model of a multileader and multifollower Stackelberg game approach and propose a bi-level hybrid multiobjective evolutionary algorithm to find optimal strategies that maximize the profit of utilities, and minimize carbon emissions in an open energy market among interconnected microsources.