Showing papers on "Ant colony published in 2008"

Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm

Abstract: This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.

Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm

Abstract: Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted.

Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization

Abstract: Recent computational developments in ant colony systems have proved fruitful for transforming discrete domains of application into continuous ones. In this paper, new combinations of an ant colony inspired algorithm (ACA) and chaotic sequences (ACH) are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that ACA and ACH handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.

Using a style-based ant colony system for adaptive learning

Abstract: Adaptive learning provides an alternative to the traditional ''one size fits all'' approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.

Two-sided assembly line balancing using an ant-colony-based heuristic

Abstract: Two-sided assembly line balancing (ALB) problems usually occur in plants which are producing large-sized high-volume products, such as buses, trucks, and domestic products. Many algorithms and heuristics have been proposed to balance the well known classical one-sided assembly lines. However, little attention has been paid to solve two-sided ALB problems. Moreover, according to our best knowledge, there is no published work in the literature on two-sided ALB problems with zoning constraints (2sALBz). In this study, an ant-colony-based heuristic algorithm is proposed for solving 2sALBz problems. This paper also makes one of the first attempts to show how an ant colony heuristic (ACH) can be applied to solve 2sALBz problems. In the paper, example applications are presented and computational experiments are performed to present the suitability of the ACH to solve 2sALBz problems. Promising results are obtained from the solution of several test problems.

Clusters of ant colonies and robust criticality in a tropical agroecosystem

Abstract: It is natural to assume that patchiness in an ecosystem must reflect an underlying property of the habitat. Yet there are several lines of evidence to suggest that intrinsic biological dynamics can produce pattern even in an ecosystem that is homogeneous for the organisms involved. Outside of the laboratory, it is difficult to convincingly demonstrate large-scale pattern formation from biological interactions, mainly because it is close to impossible to exclude habitat variables. Vandermeer et al. have used the artificiality of shade trees planted in a coffee plantation to get around this problem, and demonstrate significant patterning of a particular species of ant, Azteca instabilis, that nests in those trees. Ant population density is controlled by natural enemies, mainly a parasitic fly, but the distribution of ant nest clusters follows a strong spatial pattern despite habitat homogeneity. This paper describes and models a striking example of non-random ecological patterning over large spatial scales apparently caused by the interaction between a common species of tropical arboreal ant and one of its natural enemies under spatially homogeneous conditions. Although sometimes difficult to measure at large scales, spatial pattern is important in natural biological spaces as a determinant of key ecological properties such as species diversity, stability, resiliency and others1,2,3,4,5,6. Here we demonstrate, at a large spatial scale, that a common species of tropical arboreal ant forms clusters of nests through a combination of local satellite colony formation and density-dependent control by natural enemies, mainly a parasitic fly. Cluster sizes fall off as a power law consistent with a so-called robust critical state7. This endogenous cluster formation at a critical state is a unique example of an insect population forming a non-random pattern at a large spatial scale. Furthermore, because the species is a keystone of a larger network that contributes to the ecosystem function of pest control, this is an example of how spatial dynamics at a large scale can affect ecosystem service at a local level.

A hybrid PSO/ACO algorithm for discovering classification rules in data mining

Abstract: We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining--where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.

Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

Abstract: Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed “continuous orthogonal ant colony” (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an “adaptive regional radius” method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization — API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

Ant Colony Optimization for Least-Cost Design and Operation of Pumping Water Distribution Systems

Abstract: Developed and demonstrated in this paper is an ant colony methodology extending previous work on ant colony optimization for least-cost design of gravitational water distribution systems with a single loading case, to the conjunctive least-cost design and operation of multiple loading pumping water distribution systems. Ant colony optimization is a relatively new meta-heuristic stochastic combinatorial computational discipline inspired by the behavior of ant colonies: ants deposit a certain amount of pheromone while moving, with each ant probabilistically following a direction rich in pheromone. This behavior has been used to explain how ants can find the shortest path between their nest and a food source, and inspired the development of ant colony optimization. The optimization problem solved herein is through linking an ant colony scheme with EPANET for the minimization of the systems design and operation costs, while delivering the consumers required water quantities at acceptable pressures. The decision variables for the design are the pipe diameters, the pumping stations maximum power, and the tanks storage, while for the operation — the pumping stations pressure heads and the water levels at the tanks for each of the loadings. The constraints are domain pressures at the consumer nodes, maximum allowable amounts of water withdrawals from the sources, and tanks storage closure. The proposed scheme is explored through base runs and sensitivity analysis using two pumping water distribution systems examples.

Carbohydrate regulation in relation to colony growth in ants

Abstract: Ants and all social insects are faced with a nutritional challenge: the food entering the colony is brought by only a small number of its workers but is shared among all members of the colony. In this study, we investigated how ants maintain carbohydrates supply at both a collective and an individual level in response to changes in the concentration of available sucrose solution, colony demography and larval growth. We manipulated the concentration of sugar solutions available to ant colonies (dilute, medium and concentrated solutions) over extended periods and measured the capacity of colonies to maintain sugar supply through compensatory feeding. First, we demonstrated that ants regulated carbohydrate intake at a collective and individual level. Initially, ants consumed most and recruited fastest in response to more concentrated than to dilute sugar solutions, but over time this pattern reversed, such that the number of ants that fed and the volume ingested by each ant was a negative function of sugar concentration in the diet. Second, we found that ants became better at regulating their carbohydrate intake with the production of larvae in the nest. When the number of larvae was experimentally doubled, the ants regulated their consumption of carbohydrates more accurately than when the number of adult workers was doubled, suggesting that larvae play an important role in providing nutritional feedback to workers. Finally, we showed that ants defended a carbohydrate ;intake target' by allowing them to select among sugar solutions of different concentration.

Discrete Optimization Multi-ant colony system (MACS) for a vehicle routing problem with backhauls

Abstract: The vehicle routing problem with backhaul (VRPB) is an extension of the capacitated vehicle routing problem (CVRP). In VRPB, there are linehaul as well as backhaul customers. The number of vehicles is considered to be fixed and deliveries for linehaul customers must be made before any pickups from backhaul customers. The objective is to design routes for the vehicles so that the total distance traveled is minimized. We use multi-ant colony system (MACS) to solve VRPB which is a combinatorial optimization problem. Ant colony system (ACS) is an algorithmic approach inspired by foraging behavior of real ants. Artificial ants are used to construct a solution by using pheromone information from previously generated solutions. The proposed MACS algorithm uses a new construction rule as well as two multi-route local search schemes. An extensive numerical experiment is performed on benchmark problems available in the literature.

Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation

Abstract: An ant colony optimization (ACO) application to a fuzzy controller (FC) design, called ACO-FC, is proposed in this paper for improving design efficiency and control performance, as well as ACO hardware implementation. An FC's antecedent part, i.e., the ldquoifrdquo part of its composing fuzzy if-then rules, is partitioned in grid-type, and all candidate rule consequent values are then listed. An ant trip is regarded as a combination of consequent values selected from every rule. A pheromone matrix among all candidate consequent values is constructed. Searching for the best one among all combinations of rule consequent values is based mainly on the pheromone matrix. The proposed ACO-FC performance is shown to be better than other metaheuristic design methods on simulation examples. The ACO used in ACO-FC is based on the known ant colony system and is hardware implemented on a field-programmable gate array chip. The ACO chip application to fuzzy control of a simulated water bath temperature control problem has verified the designed chip effectiveness.

Ant colony optimization and Swarm intelligence : 6th International Conference, ANTS 2008, Brussels, Belgium, September22-24, 2008 : proceedings

Abstract: A Combined Ant Colony and Differential Evolution Feature Selection Algorithm.- A Combined Ant Colony and Differential Evolution Feature Selection Algorithm.- An Improved ACO Based Plug-in to Enhance the Interpretability of Fuzzy Rule Bases with Exceptions.- Ant Colony Optimization for Energy-Efficient Broadcasting in Ad-Hoc Networks.- Ant Colony Optimization for Genome-Wide Genetic Analysis.- cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes.- Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics.- Gathering Multiple Robotic Agents with Crude Distance Sensing Capabilities.- Integration of ACO in a Constraint Programming Language.- Learning from House-Hunting Ants: Collective Decision-Making in Organic Computing Systems.- Modeling Phase Transition in Self-organized Mobile Robot Flocks.- Molecular Structure Elucidation Using Ant Colony Optimization: A Preliminary Study.- Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search.- Simple Dynamic Particle Swarms without Velocity.- Swarming in a Virtual World: A PSO Approach to Virtual Camera Composition.- The Binary Bridge Selection Problem: Stochastic Approximations and the Convergence of a Learning Algorithm.- Two-Level ACO for Haplotype Inference Under Pure Parsimony.- What Hides in Dimension X? A Quest for Visualizing Particle Swarms.- Short Papers.- A Dynamic Swarm for Visual Location Tracking.- A Simulation Study of Routing Performance in Realistic Urban Scenarios for MANETs.- ACO-Based Scheduling of Parallel Batch Processing Machines with Incompatible Job Families to Minimize Total Weighted Tardiness.- Adaptive Particle Swarm Optimization.- Ant Based Heuristics for the Capacitated Fixed Charge Location Problem.- Ant Colony Optimization and the Single Round Robin Maximum Value Problem.- Artificial Ants to Extract Leaf Outlines and Primary Venation Patterns.- Autonomous Reconfiguration in a Self-assembling Multi-robot System.- Beanbag Robotics: Robotic Swarms with 1-DoF Units.- BlatAnt: Bounding Networks' Diameter with a Collaborative Distributed Algorithm.- Dependency by Concentration of Pheromone Trail for Multiple Robots.- Dissemination of Information with Fair Load Distribution in Self-organizing Grids.- Emergent Sorting in Networks of Router Agents.- Enhancing the Cooperative Transport of Multiple Objects.- Formal Modeling of BeeAdHoc: A Bio-inspired Mobile Ad Hoc Network Routing Protocol.- Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series.- Incorporating Preferences to a Multi-objective Ant Colony Algorithm for Time and Space Assembly Line Balancing.- KANTS: Artifical Ant System for Classification.- Lattice Formation in Space for a Swarm of Pico Satellites.- Merging Groups for the Exploration of Complex State Spaces in the CPSO Approach.- Parallel Ant Colony Optimization for the Quadratic Assignment Problems with Symmetric Multi Processing.- Social Odometry in Populations of Autonomous Robots.- The Architecture of Ant-Based Clustering to Improve Topographic Mapping.- The Small World of Pheromone Trails.- Extended Abstracts.- A Particle Swarm Optimization Algorithm for Multiuser Scheduling in HSDPA.- AntLib v1.0: A Generic C++ Framework for Ant Colony Optimization.- Applying a Distributed Swarm-Based Algorithm to Solve Instances of the RCPSP.- bicACO: An Ant Colony Inspired Biclustering Algorithm.- Dynamic Routing and Travel Time Prediction with Ant Based Control.- Network Formation Using Ant Colony Optimization.- On the Stability and the Parameters of Particle Swarm Optimization.- Regional Traffic Assignment by ACO.- SwarmClass: A Novel Data Clustering Approach by a Hybridization of an Ant Colony with Flying Insects.- The Differential Ant-Stigmergy Algorithm for Large Scale Real-Parameter Optimization.

Combining Lagrangian heuristic and Ant Colony System to solve the Single Source Capacitated Facility Location Problem

Abstract: The facility location problems have been applied extensively in practice. We describe a Multiple Ant Colony System (MACS) to solve the Single Source Capacitated Facility Location Problem (SSCFLP). Lagrangian heuristics have been shown to produce good solutions for the SSCFLP. A hybrid algorithm, which combines Lagrangian heuristic and Ant Colony System (ACS), LH–ACS, is developed for the SSCFLP. The performance of the proposed methods are tested on two sets of benchmark instances and compared with other heuristic algorithms in the literature. The computational results indicate that both MACS and LH–ACS are effective and efficient for the SSCFLP and competitive with other well-known algorithms.

Nest connectivity and colony structure in unicolonial Argentine ants

Abstract: Unicolonial ant colonies occupy many nests and individuals rarely show aggression across large geographic distances. These traits make it difficult to detect colony structure. Here we identify colony structure at scales of hundreds of square-meters, within an invasive population of unicolonial Argentine ants. In experiments using labeled food, and in a 3-year census of nests and trails, we found that food was shared and nests were linked by trails at distances up to 50 meters. Food was not distributed to all nearby Argentine ant nests, showing that ants tend to share resources within a spatially bounded group of nests. The spatial extent of food sharing increased from winter to summer. Across different habitats and nest densities, nests were consistently aggregated at spatial scales of 3- 4 meters in radius. This suggests that new nests bud from old nests at short distances regardless of local conditions. We suggest that a ‘colony’ of Argentine ants could be defined as a group of nests among which ants travel and share food. In our study population, colonies occupy up to 650 m2 and contain as many as 5 million ants. In combination with previous work showing that there is genetic differentiation among nests at similar spatial scales, the results suggest that Argentine ant populations do not function ecologically as single, large supercolonies, but instead as mosaics of smaller, distinct colonies consisting of groups of interacting nests.

Hybrid ant colony algorithms for path planning in sparse graphs

Abstract: The general problem of path planning can be modeled as a traveling salesman problem which assumes that a graph is fully connected. Such a scenario of full connectivity is however not always realistic. One such motivating example for us is the application of path planning for unmanned reconnaissance aerial vehicles (URAVs). URAVs are widely deployed for photography or imagery gathering missions of sites of interest. These sites can be targets in a combat zone to be investigated or sites inaccessible by ground transportation, such as those hit by forest fires, earthquake or other forms of natural disasters. The navigation environment is one where the overall configuration of the problem is a sparse graph. Unlike graphs that are fully connected, sparse graphs are not always Hamiltonian. In this paper, we describe hybrid ant colony algorithms (HACAs) proposed for path planning in sparse graphs since existing ant colony solvers designed for solving TSP do not apply to the present context directly. HACAs represent ant inspired algorithms incorporated with a local search procedure and some heuristic techniques for uncovering feasible route(s) or path(s) in a sparse graph within tractable time. Empirical results conducted on a set of generated sparse graphs demonstrate the excellent convergence property and robustness of HACAs in uncovering low risk and Hamiltonian visitation paths. Further, the obtained results also indicate that HACAs converge to secondary closed paths in situations where a Hamiltonian cycle does not exist theoretically or is not attainable within the bounded computational time window.

A hybrid particle swarm and ant colony optimization for design of truss structures

Abstract: This paper presents a particle swarm ant colony optimization for design of truss structures. The algorithm is based on the particle swarm optimizer with passive congregation and ant colony optimization. The particle swarm ant colony optimization applies the particle swarm optimizer with passive congregation for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. Ant colony optimization works as a local search, wherein, ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. A new relation is defined for the inertia weight, and the terminating criterion is changed in the way that after decreasing the movements of particles, the search process stops. With these changes, the number of iterations does not increase. The proposed method is tested on several benchmark trusses from literature. The result comparisons with particle swarm optimizer, particle swarm optimizer with passive congregation and other optimization algorithms demonstrate the effectiveness of the presented method.

Multiple colony ant algorithm for job-shop scheduling problem

Abstract: Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems.

Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system

Abstract: This study presents and evaluates a modified ant colony optimization (ACO) approach for the precedence and resource-constrained multiprocessor scheduling problems. A modified ant colony system is proposed to solve the scheduling problems. A two-dimensional matrix is proposed in this study for assigning jobs on processors, and it has a time-dependency relation structure. The dynamic rule is designed to modify the latest starting time of jobs and hence the heuristic function. In exploration of the search solution space, this investigation proposes a delay solution generation rule to escape the local optimal solution. Simulation results demonstrate that the proposed modified ant colony system algorithm provides an effective and efficient approach for solving multiprocessor system scheduling problems with resource constraints.

Ant colony optimisation for tree-searching-based synthesis of monopulse array antenna

Abstract: The design of simple feed networks is of great interest in synthesising monopulse radar array antennas in order to reduce the complexity of the antenna architecture, the costs as well as the occupied physical space (e.g. on aircraft). Sub-arraying techniques have been proposed to properly address such a task. Starting from the formulation of the sub-arraying problem in terms of a combinatorial one, the final compromise solution is obtained by looking for the minimum cost path inside a binary tree graph through an ant colony optimiser.

Solution approaches for the capacitated single allocation hub location problem using ant colony optimisation

Abstract: Hub and spoke type networks are often designed to solve problems that require the transfer of large quantities of commodities. This can be an extremely difficult problem to solve for constructive approaches such as ant colony optimisation due to the multiple optimisation components and the fact that the quadratic nature of the objective function makes it difficult to determine the effect of adding a particular solution component. Additionally, the amount of traffic that can be routed through each hub is constrained and the number of hubs is not known a-priori. Four variations of the ant colony optimisation meta-heuristic that explore different construction modelling choices are developed. The effects of solution component assignment order and the form of local search heuristics are also investigated. The results reveal that each of the approaches can return optimal solution costs in a reasonable amount of computational time. This may be largely attributed to the combination of integer linear preprocessing, powerful multiple neighbourhood local search heuristic and the good starting solutions provided by the ant colonies.

Specificity and transmission mosaic of ant nest-wall fungi

Abstract: Mutualism, whereby species interact to their mutual benefit, is extraordinary in a competitive world. To recognize general patterns of origin and maintenance from the plethora of mutualistic associations proves a persisting challenge. The simplest situation is believed to be that of a single mutualist specific to a single host, vertically transmitted from one host generation to the next. We characterized ascomycete fungal associates cultured for nest architecture by the ant subgenera Dendrolasius and Chthonolasius. The ants probably manage their fungal mutualists by protecting them against fungal competitors. The ant subgenera display different ant-to-fungus specificity patterns, one-to-two and many-to-one, and we infer vertical transmission, in the latter case overlaid by horizontal transmission. Possible evolutionary trajectories include a reversal from fungiculture by other Lasius subgenera and inheritance of fungi through life cycle interactions of the ant subgenera. The mosaic indicates how specificity patterns can be shaped by an interplay between host life-cycles and transmission adaptations.

Scheduling a single batch-processing machine with arbitrary job sizes and incompatible job families: An ant colony framework

Abstract: This paper investigates the first attempt on the batch-processing machine scheduling problem, where the machine can process multiple jobs simultaneously, using an ant colony optimization metaheuristic. We consider the scheduling problem of a single batch-processing machine with incompatible job families and the performance measure of minimizing total weighted completion time. Jobs of a given family have an identical processing time and are characterized by arbitrary sizes and weights. Based on a number of developed heuristic approaches, we propose an ant colony framework (ACF) in two versions, which are distinguished by the type of embedded heuristic information. Each version is also investigated in two formats, that is the pure ACF and the hybridized ACF. To verify the performance of our framework, comparisons are made based on using a set of well-known existing heuristic and meta-heuristic algorithms taken from the literature, on a diverse set of artificially generated test problem instances. Computational results show the high performance of the proposed framework and signify its ability to outperform the comparator algorithms in most cases as the problem size increases.