Recent Metaheuristics Research Articles

Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV

This paper deals with the modelling and advanced metaheuristics-based tuning of a Linear Quadratic Gaussian (LQG) controller for a particular class of convertible Unmanned Aerial Vehicles (UAVs), called Quad Tilt-Wing (QTW). A dynamical model for the vertical, transition and horizontal flights is firstly derived using the Newton–Euler formalism. Since the main design parameters in the LQG control approach are the weighting matrices which are usually selected by classical and hard trials-errors based procedures, an optimization problem under operating constraints is formulated for systematically tuning these decision variables. Such an optimization-based LQG control problem is systematically solved using advanced and recent metaheuristics such as the Harmonic Search Algorithm, Water Cycle Algorithm (WCA) and Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA). To assess the performance of the proposed algorithms and conclude about their control parameters choices, an empirical comparison study is performed for solving different test functions from the single-objective optimization literature. The classical Particle Swarm Optimization and Ant Bee Colony algorithms are retained as comparison tools. Value- and Friedmans rank-based statistical analyses are presented. After that, experiments are conducted to achieve optimal coefficients of weighting matrices of LQG controllers in solving QTW position and heading stabilization problem. Demonstrative simulations show that the WCA, outperforming each one of other metaheuristics, provides the best results in terms of robustness and performance for the problem in hand. Results verify the efficiency the proposed metaheuristics-tuned LQG control structures.

An improved HBA metaheuristic

As simple and effective optimisation approach, homogeneity-based algorithm (HBA) is one of the recent metaheuristics, proposed to minimise the total misclassification cost of data mining approaches. However, one problem is that HBA does not adopt computational complexity of the used data mining technique. This is due to the way objective function is defined. So, in this paper, we have proposed an improved HBA (IHBA), which is utilising a modified objective function that compute the computational complexity of the used classification method. We also test several clustering techniques as HBA parameters tuning, in order to enhance classifiers' performance. We have tested IHBA on different benchmarks and the obtained results show the effectiveness of the proposed method.

An improved HBA metaheuristic

As simple and effective optimisation approach, homogeneity-based algorithm (HBA) is one of the recent metaheuristics, proposed to minimise the total misclassification cost of data mining approaches. However, one problem is that HBA does not adopt computational complexity of the used data mining technique. This is due to the way objective function is defined. So, in this paper, we have proposed an improved HBA (IHBA), which is utilising a modified objective function that compute the computational complexity of the used classification method. We also test several clustering techniques as HBA parameters tuning, in order to enhance classifiers' performance. We have tested IHBA on different benchmarks and the obtained results show the effectiveness of the proposed method.

An effective discrete artificial bee colony algorithm with idle time reduction techniques for two-sided assembly line balancing problem of type-II

Two-sided assembly line is broadly utilized to manufacture high-volume large-size products. Reconfiguration of this line is a major practice in industry, which is known as two-sided assembly line balancing problem of type-II. To solve this NP-hard problem, this paper proposes an improved discrete artificial bee colony (DABC) algorithm via blending idle time reduction techniques. The cycle time compression mechanism speeds up the search process by reducing total idle times to a larger extent on earlier iterations. Task assignment rule is specifically designed to eliminate sequence-dependent idle times. The overload and underload are taken into account as a secondary objective to reduce the remaining idle times. To achieve a fine balance between the diversification and intensification, the employed bees in the DABC algorithm generate new food sources by combining the features of two solutions, and the onlookers expand the search space with the variable neighborhood search. Nine recent meta-heuristics including a simulated annealing algorithm and an ant colony optimization algorithm are also extended for the TALBP-II to test the performance of the proposed DABC algorithm. Experimental results demonstrate that the proposed method outperforms the nine compared algorithms and find 22 brand-new results for large-size problems.

Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so.

An artificial bee colony algorithm for the p-median facility location problem

Artificial bee colony ABC algorithm is one of the recent meta-heuristics used for solving combinatorial optimisation problems. In this paper, an ABC algorithm model is employed for solving the p-median problem, which is an important type of the facility location problems and belongs to the class of NP-hard problems. The algorithm has been tested and widely used. The OR-Library and Galvao p-median benchmarking problems were compared with other meta-heuristic methods from the literature. The experimentation reveals that the employed algorithm gives promising results when comparing with other meta-heuristic methods.

Minimizing Total Flow Time in Permutation Flowshop Scheduling by Two-Phase Approach

This work considered minimizing total flow time for general n-jobs and m-machines permutation flowshop scheduling problems, which are well-known to be NP-hard. The proposed approach used the combination of Decision Tree (DT) based heuristic algorithm and Scatter Search (SS) algorithm. Unlike existing heuristics available for flowshop scheduling, the DT based algorithm is used to represent the low level data into high level knowledge ie., in the form of IF-THEN else rules which are understandable by the semi-skilled worker. The SS has wide exploration of search space through diversification. The advantages of both DT and SS are utilized to form a two-phase approach. The seed solution generated from DT based approach is taken as input seed to SS algorithm to find good solution. The proposed approach is compared with most effective heuristics and recent meta-heuristics available in the literature. The proposed approach gives better solution as compared to existing composite heuristics and comparable with existing hybrid meta-heuristics.

Applications of recent metaheuristics optimisation algorithms in biomedical engineering: a review

With the increasing complexity of real-world optimisation problems, researchers from various domains of engineering sciences are constantly looking for accurate, fast and robust optimisers. Over the past few decades, studies on Metaheuristic Optimisation Algorithms (MOA) have shown that these methods can be efficiently used to eliminate most of the difficulties of classical methods. These algorithms have inherent capability to explore a large region of the solution space, are computationally robust and efficient, and can avoid premature convergence. This paper reviews some of the applications of three new algorithms, i.e. biogeography-based optimisation, cuckoo search and bat algorithm, in various domains of biomedical engineering, namely clinical diagnosis, biomedical instrumentation, artificial neural networks, biomedical image processing, bioelectronics, biological control system and biomechanics, and show how these fields have benefitted from the use of these recently introduced MOA based on evolution...

An Efficient Local Search for the Feedback Vertex Set Problem

Inspired by many deadlock detection applications, the feedback vertex set is defined as a set of vertices in an undirected graph, whose removal would result in a graph without cycle. The Feedback Vertex Set Problem, known to be NP-complete, is to search for a feedback vertex set with the minimal cardinality to benefit the deadlock recovery. To address the issue, this paper presents NewkLS FVS(LS, local search; FVS, feedback vertex set), a variable depth-based local search algorithm with a randomized scheme to optimize the efficiency and performance. Experimental simulations are conducted to compare the algorithm with recent metaheuristics, and the computational results show that the proposed algorithm can outperform the other state-of-art algorithms and generate satisfactory solutions for most DIMACSbenchmarks.

An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem

In the no-idle flowshop, machines cannot be idle after finishing one job and before starting the next one. Therefore, start times of jobs must be delayed to guarantee this constraint. In practice machines show this behavior as it might be technically unfeasible or uneconomical to stop a machine in between jobs. This has important ramifications in the modern industry including fiber glass processing, foundries, production of integrated circuits and the steel making industry, among others. However, to assume that all machines in the shop have this no-idle constraint is not realistic. To the best of our knowledge, this is the first paper to study the mixed no-idle extension where only some machines have the no-idle constraint. We present a mixed integer programming model for this new problem and the equations to calculate the makespan. We also propose a set of formulas to accelerate the calculation of insertions that is used both in heuristics as well as in the local search procedures. An effective iterated greedy (IG) algorithm is proposed. We use an NEH-based heuristic to construct a high quality initial solution. A local search using the proposed accelerations is employed to emphasize intensification and exploration in the IG. A new destruction and construction procedure is also shown. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics for the problem and conduct a comprehensive set of computational and statistical experiments with a total of 1750 instances. The results show that the proposed IG algorithm outperforms existing methods in the no-idle and in the mixed no-idle scenarios by a significant margin.

An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process

This paper aims to provide a solution method for the real-world hybrid flowshop scheduling problem resulting from a steelmaking process, which has important applications in modern iron and steel industry. We first present a mixed integer mathematic model based on a comprehensive investigation. Then, we develop a heuristic method and two improvement procedures for a given schedule based on the problem-specific characteristics. Finally, we propose an effective artificial bee colony (ABC) algorithm with the job-permutation-based representation for solving the scheduling problem. The proposed ABC algorithm incorporates the heuristic and improvement procedures as well as new characteristics including a neighboring solution generation method and two enhanced strategies. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics to the problem and conduct a serial of experiments with the instances generated according to real-world production process. The results show that the proposed ABC algorithm is more effective than all other adaptations after comprehensive computational comparisons and statistical analysis.

A variable neighbourhood search for integrated production and preventive maintenance planning in multi-state systems

This paper presents a variable neighbourhood search (VNS) to the integrated production and maintenance planning problem in multi-state systems. VNS is one of the most recent meta-heuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In the studied problem, production and maintenance decisions are co-ordinated, so that the total expected cost is minimised. We are given a set of products that must be produced in lots on a multi-state production system during a specified finite planning horizon. Planned preventive maintenance and unplanned corrective maintenance can be performed on each component of the multi-state system. The maintenance policy suggests cyclical preventive replacements of components, and a minimal repair on failed components. The objective is to determine an integrated lot-sizing and preventive maintenance strategy of the system that will minimise the sum of preventive and corrective maintenance costs, setup costs, holding costs, backorder costs and production costs, while satisfying the demand for all products over the entire horizon. We model the production system as a multi-state system with binary-state components. The formulated problem can be solved by comparing the results of several multi-product capacitated lot-sizing problems. The proposed VNS deals with the preventive maintenance selection task. Results on test instances show that the VNS method provides a competitive solution quality at economically computational expense in comparison with genetic algorithms.

Evaluating differential evolution with penalty function to solve constrained engineering problems

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results with some recent metaheuristics. Results show that the classical DE with a very simple penalty function to handle constraints is still very competitive in the tested problems.

Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II

Recent metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide this search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values. The authors’ preceding Part I study demonstrated how to improve such approaches by new inequalities that dominate those previously proposed. In Part II, the authors review the fundamental concepts underlying weighted pseudo cuts for generating guiding inequalities, including the use of target objective strategies. Building on these foundations, this paper develops a more advanced approach for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. The authors demonstrate how to produce new inequalities by “mining” reference sets of elite solutions to extract characteristics these solutions exhibit in common. Additionally, a model embedded memory is integrated to provide a range of recency and frequency memory structures for achieving goals associated with short term and long term solution strategies. Finally, supplementary linear programming models that exploit the new inequalities for intensification and diversification are proposed.

Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization Part I

Recent adaptive memory and evolutionary metaheuristics for mixed integer programming have included proposals for introducing inequalities and target objectives to guide the search. These guidance approaches are useful in intensification and diversification strategies related to fixing subsets of variables at particular values, and in strategies that use linear programming to generate trial solutions whose variables are induced to receive integer values. In Part I (the present paper), we show how to improve such approaches by new inequalities that dominate those previously proposed and by associated target objectives that underlie the creation of both inequalities and trial solutions. Part I focuses on exploiting inequalities in target solution strategies by including partial vectors and more general target objectives. We also propose procedures for generating target objectives and solutions by exploiting proximity in original space or projected space. Part II of this study (to appear in a subsequent issue) focuses on supplementary linear programming models that exploit the new inequalities for intensification and diversification, and introduce additional inequalities from sets of elite solutions that enlarge the scope of these models. Part II indicates more advanced approaches for generating the target objective based on exploiting the mutually reinforcing notions of reaction and resistance. Our work in the concluding segment, building on the foundation laid in Part I, examines ways our framework can be exploited in generating target objectives, employing both older adaptive memory ideas of tabu search and newer ones proposed here for the first time.

POPMUSIC for the point feature label placement problem

Point feature label placement is the problem of placing text labels adjacent to point features on a map so as to maximize legibility. The goal is to choose positions for the labels that do not give rise to label overlaps and that minimize obscuration of features. A practical goal is to minimize the number of overlaps while considering cartographic preferences. This article proposes a new heuristic for solving the point feature label placement problem based on the application of the POPMUSIC frame. Computational experiments show that the proposed heuristic outperformed other recent metaheuristics approaches in the literature. Experiments with problem instances involving up to 10 million points show that the computational time of the proposed heuristic increases almost linearly with the problem size. New problem instances based on real data with more than 13,000 labels are proposed.

Parallel variable neighbourhood search algorithms for job shop scheduling problems

Variable neighbourhood search (VNS) is one of the most recent metaheuristics used for solving combinatorial optimization problems in which a systematic change of neighbourhood with a local search is carried out. However, as happens with other metaheuristics, it takes a long time to reach some useful solutions while solving some sort of hard combinatorial problems such as job shop scheduling (JSS). Parallelization is one of the most considerable policies to overcome this matter. In this paper, firstly, a number of VNS algorithms are examined for JSS problems and then four different parallelization policies are taken into account to determine efficient parallelization for VNS algorithms. The experimentation reveals the performance of various VNS algorithms and the efficiency of policies to follow in parallelization. In the end, the unilateral-ring topology, a noncentral parallelization method, is found as the most efficient policy.

Particle swarm optimization and differential evolution for the single machine total weighted tardiness problem

In this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the variable neighbourhood search (VNS) is embedded in both algorithms to improve the solution quality and the computational efficiency. Ultimately, all the best known or optimal solutions of instances are found by the VNS version of both algorithms.

A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility

After 50 years of research in the field of flowshop scheduling problems the scientific community still observes a noticeable gap between the theory and the practice of scheduling. In this paper we aim to provide a metaheuristic, in the form of a genetic algorithm, to a complex generalized flowshop scheduling problem that results from the addition of unrelated parallel machines at each stage, sequence dependent setup times and machine eligibility. Such a problem is common in the production of textiles and ceramic tiles. The proposed algorithm incorporates new characteristics and four new crossover operators. We show an extensive calibration of the different parameters and operators by means of experimental designs. To evaluate the proposed algorithm we present several adaptations of other well-known and recent metaheuristics to the problem and conduct several experiments with a set of 1320 random instances as well as with real data taken from companies of the ceramic tile manufacturing sector. The results indicate that the proposed algorithm is more effective than all other adaptations.

Variable Neighbourhood Search for Job Shop Scheduling Problems

Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In this paper, an investigation on implementing VNS for job shop scheduling problems is carried out tackling benchmark suites collected from OR library. The idea is to build the best local search and shake operations based on neighbourhood structure available. The results are presented and compared with the recent approaches in the literature. It is concluded that the VNS algorithm can generally find better results.