Pure Genetic Algorithm Research Articles

Simulation-based hybrid genetic algorithms for the stochastic multi-mode resource-constrained project scheduling problem with minimized financial risk

Projects face uncertainties during implementation, and these uncertainties often lead to risk factors that disrupt successful project execution and cause a loss of profit. The stochastic project scheduling technique has been an effective avenue for reducing the risks in project management. Nevertheless, previous studies often neglected resource limits for minimizing the financial risks of projects in stochastic project scheduling problems. This study aims to optimize the conditional net present value at risk (CNPVaR), which measures a project’s expected loss of net present value (NPV) in a stochastic multi-mode resource-constrained project scheduling problem (SMRCPSP). Accordingly, considering the stochastic activity duration and cash flow, we construct a scenario-based optimization model for the SMRCPSP, where the solution (strategy) of the SMRCPSP is represented by a policy that includes an activity list and execution mode list; a scheduling policy is then employed to map the start times of activities at each decision time based on the solution. Three simulation-based hybrid genetic algorithms embedded with different iteration alternatives according to activity-based (AB) or resource-based (RB) scheduling policies are used. Furthermore, computational experiments are conducted to evaluate the proposed procedures through 480 instances. With respect to the CNPVaR, expected NPV, and algorithm stability metrics, the results of the experiments demonstrate that the genetic algorithm (GA) with local search based on the RB scheduling policy performed best compared with two other hybrids of the GA, the existing vibration damping optimization (VDO) method and a pure GA for large-sized projects. However, the VDO outperforms the other approaches according to the AB scheduling policy for small-scale projects. Additionally, a case study is conducted; the findings demonstrate that the risk attitude (confidence level) of project managers and the discount factor have impacts on the CNPVaR and expected NPV, but the CNPVaR is insensitive to the number of simulations.

Dynamic Scheduling of Multi-agent Electromechanical Production Lines based on Iterative Algorithms

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics.

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

Discriminating and Clustering Ordered Permutations Using Artificial Neural Networks: A Potential Application in ANN-Guided Genetic Algorithms

Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications. These problems naturally represent solutions as an ordered permutation of objects. However, as the number of objects increases, finding optimal permutations is extremely difficult when using exact optimization methods. In those circumstances, approximate algorithms such as metaheuristics are a plausible way of finding acceptable solutions within a reasonable computational time. In this paper, we present a technique for clustering and discriminating ordered permutations with potential applications in developing neural network-guided metaheuristics to solve this class of problems. In this endeavor, we developed two different techniques to convert ordered permutations to binary-vectors and considered Adaptive Resonate Theory (ART) neural networks for clustering the resulting binary vectors. The proposed binary conversion techniques and two neural networks (ART-1 and Improved ART-1) are examined under various performance indicators. Numerical examples show that one of the binary conversion methods provides better results than the other, and Improved ART-1 is superior to ART-1. Additionally, we apply the proposed clustering and discriminating technique to develop a neural-network-guided Genetic Algorithm (GA) to solve a flow-shop scheduling problem. The investigation shows that the neural network-guided GA outperforms pure GA.

Joint UAV Position and Power Optimization for Accurate Regional Localization in Space-Air Integrated Localization Network

Accurate location estimation of Internet-of-Things (IoT) devices within an Area of Interest (AoI) is a challenging issue, especially in a global navigation satellite system (GNSS)-constrained environment. In this article, we present a space-air integrated localization network (SAILN) architecture to exploit the advantages of the unmanned-aerial-vehicle (UAV)-based localization through joint position and power optimization (JPPO) strategies. In SAILN, UAVs can utilize their flexible movement to obtain the line-of-sight (LOS) path with a high probability, thereby providing the potential IoT devices in the AoI with supplementary localization information. The JPPO of UAVs aims to improve the regional localization accuracy for the entire AoI, considering the no-fly-zone (NFZ) and the total energy constraint. We propose the average localization accuracy increment (ALAI) of the sampling points in the AoI as the metric to measure the performance of SAILN compared with that of only satellites, which is regarded as the objective to formulate the JPPO problems for UAV operations in both static and dynamic SAILN. The intractable problems can be resolved by the pure genetic algorithm (PGA) that has a low computational cost and unique features suiting the JPPO of UAVs. Then, by taking advantage of the ALAI convexity to the UAVs’ power, we propose a power reallocation-based two-step algorithm (PRTSA) to further explore an improved JPPO solution. Simulation results validate that the proposed PRTSA can obtain a higher localization accuracy for the entire AoI than the PGA and the other straightforward baselines.

A BRKGA-based matheuristic for the maximum quasi-clique problem with an exact local search strategy

Given a graph G = (V, E) and a threshold γ ∈ (0, 1], the maximum cardinality quasi- clique problem consists in finding a maximum cardinality subset C* of the vertices in V such that the density of the graph induced in G by C* is greater than or equal to the threshold γ. This problem has a number of applications in data mining, e.g., in social networks or phone call graphs. We propose a matheuristic for solving the maximum cardinality quasi-clique problem, based on the hybridization of a biased random-key genetic algorithm (BRKGA) with an exact local search strategy. The newly proposed approach is compared with a pure biased random-key genetic algorithm, which was the best heuristic in the literature at the time of writing. Computational results show that the hybrid BRKGA outperforms the pure BRKGA.

Hybrid evolutionary algorithms and Lagrangian relaxation for multi-period star hub median problem considering financial and service quality issues

Hub facilities are centralized locations that consolidate and distribute the commodities in transportation networks. In many real world applications, transport service providers may prefer to lease hub facilities for a time horizon rather than being owned or constructed. In this paper, a modeling framework is proposed for the multi-period hub location problem that arises in the design of the star–star network with two types of hubs and links. It includes a designated static central hub, some movable hub facilities and a set of nodes with pairwise demands. A periodic growth in the amount of budget is considered at each period to expand the transportation network and an interest rate is also applied to the unused budget available during each period. Since the overall quality of services in the hub and spoke systems rely on the length of the paths, upper bound constraints are considered for the paths between nodes. Numerical experiments are carried out to show the applicability of the proposed model. Due to the computational complexity of the model, an improved genetic algorithm (GA) and a hybrid particle swarm optimization (HPSO) are utilized to find near optimal solutions. Both algorithms employ caching strategy to improve the computation times. Moreover, the HPSO benefits from genetic operators and local search methods to update the particles. In order to assess the effectiveness of the proposed methods, the results are compared with a pure GA and a proper lower bound achieved by a Lagrangian relaxation method.

Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem

In this paper we focus on an unrelated parallel workgroup scheduling problem where each workgroup is composed of a number of personnel with similar work skills which has eligibility and human resource constraints. The most difference from the general unrelated parallel machine scheduling with resource constraints is that one workgroup can process multiple jobs at a time as long as the resources are available, which means that a feasible scheduling scheme is impossible to get if we consider the processing sequence of jobs only in time dimension. We construct this problem as an integer programming model with the objective of minimizing makespan. As it is incapable to get the optimal solution in the acceptable time for the presented model by exact algorithm, meta-heuristic is considered to design. A pure genetic algorithm based on special coding design is proposed firstly. Then a hybrid genetic algorithm based on bin packing strategy is further developed by the consideration of transforming the single workgroup scheduling to a strip-packing problem. Finally, the proposed algorithms, together with exact approach, are tested at different size of instances. Results demonstrate that the proposed hybrid genetic algorithm shows the effective performance.

A Self-Gap-Correction Method for Accurate Permittivity Measurement Using the Hybrid Optimization Algorithm

A novel self-gap-correction method for accurately measuring the broadband permittivity of materials based on the transmission/reflection theory is proposed. This method relaxes the fully filled assumption of the test specimens and realizes the self-correction of air-gap effect without requiring its exact size. It is based on a reliable quantitative relationship between the “true” permittivity and the gap size. A combination strategy of the electromagnetic finite-element simulation and a hybrid optimization algorithm is employed to rapidly extract the “true” values of the gap size and permittivity. This is accomplished by minimizing the errors between the measured and the simulated scattering parameters. Furthermore, combining the advantages of the genetic algorithms (GAs) and the gradient-descent method, the hybrid optimization technique has a high computational efficiency with only one optimization variable. The high accuracy and practical suitability of the proposed method are validated through experimental tests on different materials. A good agreement is achieved between the extracted values of gap size and permittivity with the ideal “true” results. Moreover, its computation time is almost one-half less than that of the conventional pure GA method.

Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient

The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.

Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm

The human mobility is nowadays always organized in a multimodal context. However, the transport system has become more complex. Consequently, for the sake of helping passengers, building Advanced Travelers Information Systems (ATIS) has become a certain need. Since passengers tend to consider several other criteria than the travel time, an efficient routing system should incorporate a multi-objective analysis. Besides, the transport system may behave in an uncertain manner. Integrating uncertainty into routing algorithms may thus provide more robust itineraries. The main objective of this paper is to propose a Memetic Algorithm (MA) in which a Genetic Algorithm (GA) is combined with a Hill Climbing (HC) local search procedure in order to solve the multicriteria shortest path problem in stochastic multimodal networks. As transport modes, railway, bus, tram and metro are considered. As optimization criteria, stochastic travel time, travel cost, number of transfers and walking time are taken into account. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that unlike classical deterministic algorithms and pure GA and HC, the proposed MA is efficient enough to be integrated within real world journey-planning systems.

An engineering method for complex structural optimization involving both size and topology design variables

SummaryThis work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second‐level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second‐level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.

Analysis of Constraint-Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy

A multiperiod generation and transmission expansion planning (G&TEP) problem is considered. This model integrates conventional generation with renewable energy sources, assuming a stochastic approach. The proposed approach is based on a centralized planned transmission expansion. Due to the worldwide recent energy guidelines, it is necessary to generate expansion plans adequate to the forecast demand over the next years. Nowadays, in most energy systems, a public entity develops both the short and long of electricity-grid expansion planning. Due to the complexity of the problem, there are different strategies to find expansion plans that satisfy the uncertainty conditions addressed. We proposed to address the G&TEP problem with a pure genetic algorithm approach. Different constraint-handling techniques were applied to deal with two complex case studies presented. Numerical results are shown to compare the strategies used in the test systems, and key factors such as a prior initialization of population and the estimated minimum number of generations are discussed.

Linear programming assisted (not embedded) genetic algorithm for flexible jobshop scheduling with lot streaming

Developed hybrid sequential GA which is superior to pure parallel GA.Detailed the common and distinct features of the hybrid GA and the pure GA.Distinguished two levels of hybridization of metaheuristics with Linear programming.Evaluated two alternative implementation strategies in hybridizing GA with LP. The hybridization of metaheuristics with other techniques for optimization has been one of the most interesting trends. The focus of research on metaheuristics is also becoming problem oriented rather than algorithm oriented. This has led researchers to try combining different algorithmic components in order to design more powerful algorithms. In this paper, we developed a linear programming assisted genetic algorithm for solving a flexible jobshop lot streaming problem. The genetic algorithm searches over both discrete and continuous variables in the problem solution space. A linear programming is used to assist the genetic algorithm by further refining promising solutions in a population periodically through determining the optimal values of the continuous variables corresponding to those promising solutions. This is different from one common way of hybridization referred to as linear programming embedded metaheuristics where the algorithm searches only over the integer variables and a linear programming subproblem is solved corresponding to every solution visited, which can be computationally prohibitive. Numerical examples showed that the proposed linear programming assisted (not embedded) genetic algorithm is superior to the embedded approach and as well as to a resource intensive multi-population pure parallel genetic algorithm.

Improvement of hydrological model calibration by selecting multiple parameter ranges

Abstract. The parameters of hydrological models are usually calibrated to achieve good performance, owing to the highly non-linear problem of hydrology process modelling. However, parameter calibration efficiency has a direct relation with parameter range. Furthermore, parameter range selection is affected by probability distribution of parameter values, parameter sensitivity, and correlation. A newly proposed method is employed to determine the optimal combination of multi-parameter ranges for improving the calibration of hydrological models. At first, the probability distribution was specified for each parameter of the model based on genetic algorithm (GA) calibration. Then, several ranges were selected for each parameter according to the corresponding probability distribution, and subsequently the optimal range was determined by comparing the model results calibrated with the different selected ranges. Next, parameter correlation and sensibility were evaluated by quantifying two indexes, RC Y, X and SE, which can be used to coordinate with the negatively correlated parameters to specify the optimal combination of ranges of all parameters for calibrating models. It is shown from the investigation that the probability distribution of calibrated values of any particular parameter in a Xinanjiang model approaches a normal or exponential distribution. The multi-parameter optimal range selection method is superior to the single-parameter one for calibrating hydrological models with multiple parameters. The combination of optimal ranges of all parameters is not the optimum inasmuch as some parameters have negative effects on other parameters. The application of the proposed methodology gives rise to an increase of 0.01 in minimum Nash–Sutcliffe efficiency (ENS) compared with that of the pure GA method. The rising of minimum ENS with little change of the maximum may shrink the range of the possible solutions, which can effectively reduce uncertainty of the model performance.

Hybrid Optimization Algorithm to Combine Neural Network for Rainfall-Runoff Modeling

Rainfall-runoff modeling is very important for Water Resources Management because accurate and timely prediction can avoid accidents, such as the life risk, economic losses, etc. This paper proposed the novel hybrid optimization algorithm to combine Neural Network (NN) for rainfall-runoff modeling, namely HGASA-NN. Firstly, a novel and specialized hybrid optimization strategy by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA) was used to train the initial connection weights and thresholds of NN. Secondly, the Back Propagation (BP) algorithm was adjusted the final weights and biases. Finally, a numerical example of daily rainfall-runoff data was used to elucidate the forecasting performance of the proposed HGASA-NN model. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the BP-NN and pure GA training NN model. Therefore, the HGASA-NN model is a promising alternative for rainfall-runoff forecasting.

Comparison between hybridized algorithm of GA–SA and ABC, GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.

A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.

Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

ABSTRACTAlmost all optimization techniques are restricted by the problems' dimensions and large search spaces. This research focuses on a special hybrid method combining two meta-heuristic techniques, genetic algorithms (GA) and particle swarm optimization (PSO) that aims at overcoming this issue. This method investigates the potential impact of constraints (or the feasible regions) on the search pattern of GA and PSO. The proposed algorithm was applied for parameter estimation of batch fermentation process for alkaline protease production by Bacillus licheniformis in submerged culture. Furthermore, a comparison of proposed hybrid GA/PSO with pure GA and pure PSO was carried out. The results revealed that combination of these two meta-heuristic algorithms speeds up the search (about two-fold faster) in comparison to pure algorithms, since it benefits from synergy. Hence, the proposed method can be considered as an applicable method for parameter estimation of biological models in particular for large search space problems. Also, it was concluded that PSO has a slightly better performance and possesses better convergence and computational time than GA.