Metaheuristic-based Algorithms Research Articles

Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.

Various Metaheuristic-Based Algorithms for Optimal Relay Coordination: Review and Prospective

The coordinated or selective power system can be considered as a sequence procedure among two protective devices installed in series and having certain features. The coordination of protective devices in protection system is a very substantial procedure to guarantee a strength and reliable power system. Directional overcurrent relays coordination is considered as an optimization problem. Due to the difficulties that face this problem especially in multi-loop networks; it is a complicated challenge taking into account the problem constraints. An overview of various metaheuristic-based optimization methodologies is presented to get a proper solution for the coordination problem between each relay pairs to guarantee satisfactory relay settings. In addition, offering various methods of relay coordination, a comparison between earlier works is also performed. It's expected that this study will be helpful for researchers interested in this field.

Optimal Control Implementation with Terminal Penalty Using Metaheuristic Algorithms

Optimal control problems can be solved by a metaheuristic based algorithm (MbA) that yields an open-loop solution. The receding horizon control mechanism can integrate an MbA to produce a closed-loop solution. When the performance index includes a term depending on the final state (terminal penalty), the prediction’s time possibly surpasses a sampling period. This paper aims to avoid predicting the terminal penalty. The sequence of the best solution’s state variables becomes a reference trajectory; this one is used by a tracking structure that includes the real process, a process model (PM) and a tracking controller (TC). The reference trajectory must be followed up as much as possible by the real trajectory. The TC makes a one-step-ahead prediction and calculates the control inputs through a minimization procedure. Therefore the terminal penalty’s calculation is avoided. An example of a tracking structure is presented. The TC may also use an MbA for its minimization procedure. The implementation is presented in two versions: using a simulated annealing algorithm and an evolutionary algorithm. The simulations have proved that the proposed approach is realistic. The tracking structure does or does not work well, depending on the PM’s accuracy in reproducing the real process.

Multi-objective cluster analysis using a gradient evolution algorithm

Data analysis becomes more important since rapid technology developments. In data analysis, data clustering is one of the very useful approaches. It can reveal important information hiding inside the dataset by organizing the instances based on their similarity. The objectives of data clustering are maximizing dissimilarity between clusters and minimizing dissimilarity within clusters. In order to construct a good clustering results, many clustering algorithms have been proposed, including the metaheuristic-based clustering algorithms. Recently, a new metaheuristic algorithm named gradient evolution has been proposed. This algorithm shows a good performance on solving the optimization problems. Therefore, this paper employs this GE algorithm for solving the clustering problem. In order to obtain a better clustering result, this paper considers multi-objective clustering instead of single-objective clustering. In this paper, the original GE algorithm is improved so then it is suitable for the multi-objective problem. The proposed modification includes the procedure for vector updating and jumping which involves Pareto rank assignment. In addition, it also employs K-means algorithm to provide the final clustering result. The proposed algorithm is verified using some benchmark datasets. It is also compared with some other multi-objective metaheuristic-based clustering algorithms. The experimental results show that the proposed algorithm can obtain better results than other metaheuristic-based algorithms.

An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem

The demand for international seaborne trade has substantially increased over the last three decades and is predicted to continue increasing during the upcoming years. A marine container terminal, as an important node in supply chains, should be able to successfully cope with increasing demand volumes. Berth scheduling can significantly influence the general throughput of marine container terminals. In this study, a mixed-integer linear programming mathematical model is proposed for the berth scheduling problem, aiming to minimize the summation of waiting costs, handling costs, and late departure costs of the vessels that are to be served at a marine container terminal. An innovative Evolutionary Algorithm is designed to solve the developed mathematical model. The proposed solution algorithm relies on the augmented self-adaptive parameter control strategy, which is developed in order to effectively change the algorithmic parameters throughout the search process. Performance of the designed algorithm is evaluated against nine alternative state-of-the-art metaheuristic-based algorithms, which have been frequently used for berth scheduling in the marine container terminal operations literature. The results demonstrate that all the developed algorithms have a high level of stability and return competitive solutions at convergence. The computational experiments also prove superiority of the designed augmented self-adaptive Evolutionary Algorithm over the alternative algorithms in terms of different performance indicators.

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

A New Hybrid Technique for Minimizing Power Losses in a Distribution System by Optimal Sizing and Siting of Distributed Generators with Network Reconfiguration

Transformations are taking place within the distribution systems to cope with the congestions and reliability concerns. This paper presents a new technique to efficiently minimize power losses within the distribution system by optimally sizing and placing distributed generators (DGs) while considering network reconfiguration. The proposed technique is a hybridization of two metaheuristic-based algorithms: Grey Wolf Optimizer (GWO) and Particle Swarm Optimizer (PSO), which solve the network reconfiguration problem by optimally installing different DG types (conventional and renewable-based). Case studies carried out showed the proposed hybrid technique outperformed each algorithm operating individually regarding both voltage profile and reduction in system losses. Case studies are carried to measure and compare the performance of the proposed technique on three different works: IEEE 33-bus, IEEE 69-bus radial distribution system, and an actual 78-bus distribution system located at Cairo, Egypt. The integration of renewable energy with the distribution network, such as photovoltaic (PV) arrays, is recommended since Cairo enjoys an excellent actual record of irradiance according to the PV map of Egypt.

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

Late acceptance hill climbing algorithm for solving patient admission scheduling problem

This article tackles a patient admission scheduling using Late Acceptance Hill Climbing Algorithm (LAHC). The LAHC algorithm is among the newly proposed metaheuristic-based algorithm which belongs to the one-point solution technique. Patient admission scheduling is a complex combinatorial optimization problem which have been proven to belongs to class of NP-hard problem in almost all its variations. This problem is concerns with assigning a set of patients arriving for medical services in the hospital to a set of rooms, timeslots and beds subject to satisfying a set of predefined constraints. The proposed adaptation of LAHC to the patient admission scheduling named LAHC-based PAS comes in two stages: the first stage involves generating of initial feasible solution using the room oriented-based approach (ROP), while the second stage utilizes three neighbourhood structures which are embedded within the component of LAHC-based PAS to further enhances the initial solution generated at the initial stage. The proposed LAHC method is evaluated using the standard benchmark dataset. The experimental results shows that the proposed method is an effective technique for tackling the PAS problem. It is observed that the method outperform many of the existing techniques when compared with the state-of-the-arts.

Multiple-Reservoir Scheduling Using β-Hill Climbing Algorithm

Abstract The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm.

A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm

This study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm that uses a new adaptive step-size adjustment strategy to improve the performance of the search process.Search mechanism of the VS is inspired by the vortex pattern, so it is called a “Vortex Search” algorithm. The original VS is an improved way of solving single-objective continuous problems. To improve the MOVS algorithm, the VS algorithm is enhanced with added calculation approaches, such as fast-nondominated-sorting and crowding-distance, in order to identify the degree of non-dominance of the solutions and the densities of their occurrence.In addition, a crossover operation is added to the MOVS algorithm in order to enhance the Pareto front convergence capacity of the solutions. Finally, to spread the solutions more successfully over the Pareto front, it has been randomly produced using the inverse incomplete gamma function using a parameter between 0 and 1. The proposed MOVS algorithm is tested against 36 different benchmark problems together with NSGAII, MOCell, IBEA and MOEA/D algorithms. The test results indicate that the MOVS algorithm achieves a better performance on accuracy and convergence speed than any other algorithms when comparisons are made against several test problems, and they also show that it is a competitive algorithm.

A metaheuristic based on the tabu search for hardware-software partitioning

Several metaheuristics have become increasingly interesting in solving combinatorial problems. In this paper, we present an algorithm involving a metaheuristic based on tabu search and binary search trees to address the problem of hardware-software partitioning. Metaheuristics do not guarantee an optimum solution, but they can produce acceptable solutions in a reasonable time. Our proposed algorithm seeks to find the efficient hardware-software partitioning that minimizes the logic area of a system on a programmable chip under the condition of time constraints. Our goal is to have a better trade-off between the logic area of the application and its execution time. Finally, we compare our algorithm to some metaheuristic-based algorithms.

Generic framework for mining cellular automata models on protein-folding simulations.

Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.

Reconfiguration of Radial Electric Power Distribution System via a Scatter Search Algorithm

In this paper, to solve the reconfiguration problem of radial distribution systems a scatter search, which is a metaheuristic-based algorithm, is proposed. In the codification process of this algorithm a structure called node-depth representation is used. It then, via the operators and from the electrical power system point of view, results finding only radial topologies. In order to show the effectiveness, usefulness, and the efficiency of the proposed method, a commonly used test system, 135-bus, and a practical system, a part of Sao Paulo state's distribution network , 7052 bus, are conducted. Results confirm the efficiency of the proposed algorithm that can find high quality solutions satisfying all the physical and operational constraints of the problem.

Multiple parts process planning in serial–parallel flexible flow lines: part II—solution method based on genetic algorithms with fixed- and variable-length chromosomes

Multiple parts process planning (MPPP) is a hard optimization problem that requires the rigor and intensity of metaheuristic-based algorithms such as simulated annealing and genetic algorithms. In this paper, a solution method for this problem is developed based on genetic algorithms. Genetic algorithms solve problems by exploring a given search space. To do this, a landscape over which the search traverses is constructed based on a number of algorithm choices. Key algorithm choices include (a) type of chromosome representation, which affects the efficiency of an algorithm, and (b) type and form of genetic operators, which affect the effectiveness of an algorithm. More specifically, the suitability of a variable-length chromosome (VLC) representation for encoding a solution to a MPPP problem is investigated. The effectiveness and efficiency of implementing the VLC algorithm is analyzed and compared with: (a) the commonly used fixed-length chromosome representation, (b) a variant of the simulated annealing algorithm, and (c) a knowledge-informed simulated annealing algorithm. The scalability of the algorithms is analyzed and their effectiveness demonstrated by experimental results based on four problem sizes. Obtained results show that, although there are variances in performances, all algorithms investigated are capable of obtaining good solutions. In addition, variances were observed for different aspects of the MPPP problem. The results indicate that the VLC algorithm is effective in solving MPPP problems that consider multiple aspects in the search for optimal process planning solutions.

Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem

This paper provides a mathematical treatment of the NP-hard post enrolment-based course timetabling problem and presents a powerful two-stage metaheuristic-based algorithm to approximately solve it. We focus particularly on the issue of solution space connectivity and demonstrate that when this is increased via specialised neighbourhood operators, the quality of solutions achieved is generally enhanced. Across a well-known suite of benchmark problem instances, our proposed algorithm is shown to produce results that are superior to all other methods appearing in the literature; however, we also make note of those instances where our algorithm struggles in comparison to others and offer evidence as to why.

Capacity improvement using simulation optimization approaches: A case study in the thermotechnology industry

In manufacturing systems, optimal buffer allocation has a considerable impact on capacity improvement. This study presents a simulation optimization procedure to solve the buffer allocation problem in a heat exchanger production plant so as to improve the capacity of the system. For optimization, three metaheuristic-based search algorithms, i.e. a binary-genetic algorithm (B-GA), a binary-simulated annealing algorithm (B-SA) and a binary-tabu search algorithm (B-TS), are proposed. These algorithms are integrated with the simulation model of the production line. The simulation model, which captures the stochastic and dynamic nature of the production line, is used as an evaluation function for the proposed metaheuristics. The experimental study with benchmark problem instances from the literature and the real-life problem show that the proposed B-TS algorithm outperforms B-GA and B-SA in terms of solution quality.

To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms

While many papers propose innovative methods for constructing individual rules in separate-and-conquer rule learning algorithms, comparatively few study the heuristic rule evaluation functions used in these algorithms to ensure that the selected rules combine into a good rule set. Underestimating the impact of this component has led to suboptimal design choices in many algorithms. The main goal of this paper is to demonstrate the importance of heuristic rule evaluation functions by improving existing rule induction techniques and to provide guidelines for algorithm designers. We first select optimal heuristic rule learning functions for several metaheuristic-based algorithms and empirically compare the resulting heuristics across algorithms. This results in large and significant improvements of the predictive accuracy for two techniques. We find that despite the absence of a global optimal choice for all algorithms, good default choices can be shared across algorithms with similar search biases. A near-optimal selection can thus be found for new algorithms with minor experimental tuning. Lastly, a major contribution is made towards balancing a model's predictive accuracy with its comprehensibility. We construct a Pareto front of optimal solutions for this trade-off and show that gains in comprehensibility and/or accuracy are possible for the techniques studied. The parametrized heuristics enable users to select the desired balance as they offer a high flexibility when it comes to selecting the desired accuracy and comprehensibility in rule miners.

Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm

The order acceptance and scheduling (OAS) problem is an important topic for make-to-order production systems with limited production capacity and tight delivery requirements. This paper proposes a new algorithm based on Artificial Bee Colony (ABC) for solving the single machine OAS problem with release dates and sequence-dependent setup times. The performance of the proposed ABC-based algorithm was validated by a benchmark problem set of test instances with up to 100 orders. Experimental results showed that the proposed ABC-based algorithm outperformed three state-of-art metaheuristic-based algorithms from the literature. It is believed that this study successfully demonstrates a high-performance algorithm that can serve as a new benchmark approach for future research on the OAS problem addressed in this study.

Metaheuristics for scheduling a no-wait flowshop manufacturing cell with sequence-dependent family setups

This paper examines the no-wait flowshop manufacturing cell scheduling problem (FMCSP) with sequence-dependent family setup times. To the best of our knowledge, the present study is among the first to investigate FMSCPs with no-wait consideration, though it is a necessary production constraint in many real-world applications. In view of the strongly NP-hard nature of this problem, three metaheuristic-based algorithms were proposed and empirically evaluated for effectively finding optimal schedules. The experimental results demonstrate that the proposed algorithms are effective and efficient at finding good quality solutions for the FMCSP with makespan criterion.