Hybrid Evolutionary Algorithm Research Articles

Objective space division-based hybrid evolutionary algorithm for handing overlapping solutions in combinatorial problems.

Overlapping solutions occur when more than one solution in the space of decisions maps to the same solution in the space of objectives. This situation threatens the exploration capacity of Multi-Objective Evolutionary Algorithms (MOEAs), preventing them from having a good diversity in their population. The influence of overlapping solutions is intensified on multi-objective combinatorial problems with a low number of objectives. This paper presents a hybrid MOEA for handling overlapping solutions that combines the classic NSGA-II with a strategy based on Objective Space Division (OSD). Basically, in each generation of the algorithm, the objective space is divided into several regions using the nadir solution calculated from the current generation solutions. Furthermore, the solutions in each region are classified into non-dominated fronts using different optimization strategies in each of them. This significantly enhances the achieved diversity of the approximate front of non-dominated solutions. The proposed algorithm (called NSGA-II/OSD) is tested on a classic Operations Research problem: the Multi-Objective Knapsack Problem (0-1 MOKP) with two objectives. Classic NSGA-II, MOEA/D and Global WASF-GA are used to compare the performance of NSGA-II/OSD. In the case of MOEA/D two different versions are implemented, each of them with a different strategy for specifying the reference point. These MOEA/D reference point strategies are thoroughly studied and new insights are provided. This paper analyses in depth the impact of overlapping solutions on MOEAs, studying the number of overlapping solutions, the number of solution repairs, the hypervolume metric, the attainment surfaces and the approximation to the real Pareto front, for different sizes of 0-1 MOKPs with two objectives. The proposed method offers very good performance when compared to the classic NSGA-II, MOEA/D and Global WASF-GA algorithms, all of them well-known in the literature.

Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

An island-based hybrid evolutionary algorithm for caloric-restricted diets

The most popular and successful way to maintain a healthy body is to have a rich and balanced diet combined with physical exercise. Since the diet dilemma was proposed, several works in the literature suggested calculating a diet that respects the nutritional needs of each person. In the Caloric-Restricted Diet Problem (CRDP), the goal is to find a reduced-calorie diet that meets these nutritional needs, enabling weight loss. This paper proposes an Island-Based Hybrid Evolutionary Algorithm (IBHEA) that uses a Genetic Algorithm (GA) and a Differential Evolution (DE) Algorithm with different parameters settings in different islands communicating through several migration policies to solve the CRDP. Computational experiments showed that IBHEA outperformed more than 5% compared with non-distributed and non-hybrid implementations, generating a greater variety of diets with a small calorie count.

Application of a hybrid evolutionary algorithm to resource-constrained parallel machine scheduling with setup times

We address the problem of resource-constrained parallel machine scheduling with setup times in the practical context of microelectronic components manufacturing. This NP-hard problem is addressed using a biased random-key genetic algorithm hybridized with tailored local search procedures organized using variable neighborhood descent. The only benchmark available in the literature is utilized, and the optimal results are presented for all instances. Two new sets with 270 challenging instances are proposed to assess the quality of the solutions reported by the proposed method. A series of experiments are conducted to generate lower and upper bounds, including four models, two list processing heuristics from the literature and an implementation of a general variable neighborhood search metaheuristic. The bounds are used as reference values. The average percentage distances from the lower and upper bounds were 22.44% and −7.62%, respectively.

CS- and GA-based hybrid evolutionary sampling algorithm for large-scale social networks

CS- and GA-based hybrid evolutionary sampling algorithm for large-scale social networks

PRIORITIZATION OF FOREST FIRE HAZARD RISK SIMULATION USING HYBRID GREY RELATIVITY ANALYSIS (HGRA) AND FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP) COUPLED WITH MULTICRITERIA DECISION ANALYSIS (MCDA) TECHNIQUES – A COMPARATIVE STUDY ANALYSIS

Forests are important dynamic systems which are widely attracted by wild fires worldwide. Due to the complexity and non-linearity of the causative forest fire problems, employing sophisticated hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threats. This estimate will provide the outline of priority areas for preventing activities and allocation of fire fighters’ stations, seeking to minimize possible damages caused by fires. This study aims at prioritizing the forest fire risk of Wassa West district of Ghana. The study considered static causative factors such as Land use and land cover (which include forest, built-ups and settlement areas), slope, aspect, linear features (water bodies and roads) and dynamic causative factors such as wind speed, precipitation, and temperature were used. The methods employed include a Hybrid Grey Relativity Analysis (HGRA) and Fuzzy Analytical Hierarchy Process (FAHP) techniques. The fuzzy sets integrated with AHP in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. FAHP and HGRA methods were used for estimating the importance (weights) of the effective factors in forest fire modelling. Based on their modelling methods, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas were analyzed based on FAHP and HGRA. The major criteria models and fire risk model were presented based on these FAHP and HGRA weights. On the other hand, the spatial data of the sub criteria were provided and assembled in GIS environment to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risks of its classes to fire occurrence. The maps of each major criterion were obtained by weighted overlay of its sub criteria maps considering to major criterion model in GIS environment. Finally, the map of fire risk was obtained by weighted overlay of major criteria maps considering to fire risk model in GIS. The results showed that the FAHP model showed superiority than HGRA in prioritizing forest fire risk of the study area in terms of statistical analysis with a standard deviation of 0.09277 m as compared to 0.1122 m respectively. The obtained fire risk map can be used as a decision support system for predicting of the future trends in the study area. The optimized structures of the proposed models could serve as a good alternative to traditional forest predictive models, and this can be a promisingly testament used for future planning and decision making in the proposed areas.

Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming-Firefly Algorithm

The cost of energy losses analysis for distributed generation (DG) is presented in this paper using a Hybrid Evolutionary Programming-Firefly Algorithm (EPFA). The proposed method was created to determine the optimal DG sizing in the distribution system while accounting for the system’s energy losses. This study presents an investigation into hybrid optimization techniques for DG capabilities and optimal operating strategies in distribution systems. The objectives of this study were to reduce the cost of energy losses while increasing the voltage profile and minimize distribution system losses. In this study, the analysis was done by consider DG type I which is DG-PV. The suggested methodology was tested using the IEEE 69-bus test system, and the simulation was written in the MATLAB programming language. Power system planners can use appropriate location and sizing from the results obtained for utility planning in terms of economic considerations. From the simulation, the result shows the proposed method can identify the suitable sizing of DG while reduce cost of energy losses and total losses in the system.

Implications of flow regulation for habitat conditions and phytoplankton populations of the Nakdong River, South Korea

Anthropogenic regulation of hydrographs is a widespread approach to river management; however, the effects of river regulation on habitat conditions and aquatic communities have rarely been studied. In this study, we analyzed the physical, chemical, and biological data from the lower Nakdong River in South Korea from 2005 to 2009 before weir construction and from 2012 to 2016 after weir construction. A partial least square path model (PLS-PM) was applied to delineate the complex interrelationships of diatoms and cyanobacteria with physicochemical parameters, nutrients, zooplankton grazing, and hydrological parameters. Inferential modeling using the hybrid evolutionary algorithm (HEA) allowed the identification of differences in the importance and threshold conditions of population dynamics drivers of diatoms and cyanobacteria before and after flow regulation. The annually averaged trajectories of limnological variables displayed significant shifts in seasonality and magnitudes of phytoplankton, zooplankton, and nutrient concentrations between the two periods. The results of PLS-PM indicated that, after flow regulation, diatoms and cyanobacteria were directly affected by nutrients and zooplankton densities and the path coefficients of hydrological parameters decreased or even were insignificant. The inferential models suggested that diatom dynamics were essentially shaped by threshold conditions of water temperature (WT) and pH before regulation, but mainly by those of rotifers (below 51.1 ind. L−1) after regulation. As for cyanobacteria dynamics, WT was identified as a critical threshold condition before and after regulation, and the threshold of PO4− concentration above 145.4 L−1 was identified as the reason for occasional blooms during the post-regulation period. Overall, the results suggest that flow regulation gradually alters habitat conditions typically of rivers to those of stagnant waters. These findings must be taken into account for sustainable management strategies of regulated rivers.

A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines

A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

In this paper, we address the weighted multi-objective re-entrant flow-shop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms.

Hybrid Evolutionary Algorithm based Task Scheduling Mechanism for Resource Allocation in Cloud Environment

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data

The last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.

MILP formulation and hybrid evolutionary algorithms for the glass container industry problem with multiple furnaces

The present paper approaches a production planning problem in the glass container industry (GCI) to redesign the production process. The redesign defines new furnaces and related molding machines, considering demand requirements or expansion plans for the industrial plant. First, we introduce a mixed-integer linear programming (MILP) model for such a problem. Next, we compare the results achieve by an exact method and two hybrid evolutionary algorithms when solving the problem. The exact method is the branch & cut (B&C) algorithm from the CPLEX solver that executes over the MILP model. The genetic algorithms are a standard genetic algorithm (GA) and a multi-population genetic algorithm (MPGA). Both methods evolve binary variables whose values allow solving the linear program (LP) model obtained from MILP. A greedy heuristic is also integrated to GA and MPGA to build an initial feasible solution for the LP model. A set of instances is generated with data provided by a GCI. The results indicate that B&C returns few optimal solutions for small-size problems, while GA and MPGA can solve large problems with relevant results in terms of solution quality and execution time.

New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method

Limited hydrogeological data accessibility leads scholars to improve the robustness of present qualitative groundwater vulnerability assessment methods using mathematical techniques. In the present study, we implemented three GIS-based groundwater vulnerability assessment indices, namely DRASTIC (Depth to water table, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), SINTACS (Soggicenza, Infiltrazione, Non saturo, Tipologia della copertura, Acquifero, Conducibilità, and Superficie topografica), and GODS (Groundwater confinement, Overlying strata, Depth to groundwater, and Soil media) to assess the groundwater vulnerability levels. Although DRASTIC results showed better performance with respect to the nitrate concentration data from 50 observation wells in the study site, the index is still unreliable due to its inherent drawbacks, including subjectivity. Hybrid PSO-GA method is a successful optimization algorithm gathering the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) while avoiding their shortcomings. The DRASTIC weighting system is optimized using PSO-GA optimization algorithm. Also, Step-wise Weight Assessment Ratio Analysis (SWARA) as a Multi-Attribute Decision Making (MADM) method is applied for changing ranges of DRASTIC rates and weights. The vulnerability indices obtained from SWARA-SWARA, DRASTIC-PSO-GA, and SWARA-PSO-GA frameworks are evaluated and compared with generic DRASTIC regarding the nitrate concentration dataset by employing Area Under the ROC Curve (AUC) and Grey relational analysis methods. Results show a noticeable improvement of correlation between indices and observed nitrate concentration after modifications and optimizations. The new hybrid SWARA-PSO-GA framework is the most effective framework in assessing the vulnerability of the present study area.

A hybrid evolutionary search for the generalized quadratic multiple knapsack problem

Knapsack problems are useful models that can formulate many real-life applications. The generalized quadratic multiple knapsack problem (GQMKP) extends the well-known quadratic multiple knapsack problem by taking into account setups and knapsack preference of the items. In this study, an efficient hybrid evolutionary search algorithm (HESA) is proposed to tackle GQMKP, which relies on a knapsack-based crossover operator to generate new offspring solutions, and an adaptive feasible and infeasible tabu search to improve new offspring solutions. Other new features of HESA include a dedicated strategy to ensure a diversified and high-quality initial population, and a streamlining technique to speed up the evaluations of candidate solutions. The experiments on two sets of 96 benchmark instances as well as one large-scale real-life instance show that the proposed algorithm outperforms the state-of-the-art algorithms from the literature. In particular, HESA finds 44 improved best-known solutions (new lower bounds) (for more than 45% cases). The key components of the algorithm are studied to assess their effects on the algorithm’s performance.

Automation of Data Flow Class Testing Using Hybrid Evolutionary Algorithms

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations.

A hybrid evolutionary algorithm for stability analysis of 2-area multi-non-conventional system with communication delay and energy storage

The integration of renewable energy with the grid is essential given the environmental and economic constraints. In this work, the dynamic performance of Automatic Generation Control (AGC) was studied with a dynamic steam turbine and renewable sources. A 2-area multi-non-conventional source (Thermal-Hydro-Solar-wind-Gas-battery storage) system with communication time delay (τ) is implemented with the proposed cascaded PID (C-PID) controller scheme. The parameters of the C-PID controller were tuned by a hybrid Improved Teaching Learning Based Optimization and Differential Evolution (hITLBO-DE).Root locus, Bode plot, and eigenvalue analysis were used to demonstrate the stability of the system. Various controllers (i.e. I, PI, PID, and C-PID) were implemented with the designed system under varying operating conditions of step (1% to 10%) and dynamic load variation (1%, ±1%). The C-PID controller is able to maintain a stable system response even for τ of 4 s. Finally, the obtained Amelioration (%) of 55.93%, 49.03%, and 72.84% for Over Shoot emphases the efficacy of the proposed C-PID controller and hITLBO-DE algorithm.

A Tabu Search and Hybrid Evolutionary Strategies Algorithms for the Integrated Process Planning and Scheduling with Due-date Agreement

There are three important functions of manufacturing which are process planning, scheduling and due date assignment. Instead of executing these functions separately, combining them together helps making more realistic and applicable plans. In this problem context random search, semi-tabu, random/semi-tabu hybrid, evolutionary strategies, and random/evolutionary strategies hybrid methods are utilized in solution. Different sized job shops are studied for performance tracking. As a result of study differences between individual solutions and integrated solutions was revealed. It is found that integrating these functions are advantageous in terms of total performance measure, and thus customer satisfaction. Random search is better than ordinary solutions. On the other hand, semi-tabu, evolutionary strategies and their hybrids are outperformed random search. Hybrid search methods are found promising.

Revisiting the performance of evolutionary algorithms

The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research and application demonstrates improved performance for domain-specific challenges. However, developing a new algorithm or comparison and selection of existing EAs for challenges in the field of optimization is relatively unexplored. The performance of different well-established algorithms is, therefore, investigated in this work. The selection of algorithms using nonparametric tests encompasses different categories to include- Genetic Algorithm, Particle Swarm Optimization, Harmony Search Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Firefly algorithm, Differential Evolution, and Artificial Bee Colony. These algorithms are applied to solve test functions, including unconstrained, constrained, industry specific problems, CEC 2011 real world optimization problems and selected CEC 2013 benchmark test functions. The three distinct performance metrics, namely, efficiency, reliability, and quality of solution derived using the quantitative attributes are provided to evaluate the performance of the employed EAs. The categorical assignment of performance attributes helps to compare different algorithms for a specific optimization problem while the performance metrics are useful to provide the common platform for new or hybrid EA development.