Showing papers on "Metaheuristic published in 2019"

Butterfly optimization algorithm: a novel approach for global optimization

Abstract: Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.

Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

Abstract: This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.

Metaheuristic research: a comprehensive survey

Abstract: Because of successful implementations and high intensity, metaheuristic research has been extensively reported in literature, which covers algorithms, applications, comparisons, and analysis. Though, little has been evidenced on insightful analysis of metaheuristic performance issues, and it is still a “black box” that why certain metaheuristics perform better on specific optimization problems and not as good on others. The performance related analyses performed on algorithms are mostly quantitative via performance validation metrics like mean error, standard deviation, and co-relations have been used. Moreover, the performance tests are often performed on specific benchmark functions—few studies are those which involve real data from scientific or engineering optimization problems. In order to draw a comprehensive picture of metaheuristic research, this paper performs a survey of metaheuristic research in literature which consists of 1222 publications from year 1983 to 2016 (33 years). Based on the collected evidence, this paper addresses four dimensions of metaheuristic research: introduction of new algorithms, modifications and hybrids, comparisons and analysis, and research gaps and future directions. The objective is to highlight potential open questions and critical issues raised in literature. The work provides guidance for future research to be conducted more meaningfully that can serve for the good of this area of research.

Atom search optimization and its application to solve a hydrogeologic parameter estimation problem

Abstract: In recent years, various metaheuristic optimization methods have been proposed in scientific and engineering fields In this study, a novel physics-inspired metaheuristic optimization algorithm, atom search optimization (ASO), inspired by basic molecular dynamics, is developed to address a diverse set of optimization problems ASO mathematically models and mimics the atomic motion model in nature, where atoms interact through interaction forces resulting from the Lennard-Jones potential and constraint forces resulting from the bond-length potential The proposed algorithm is simple and easy to implement ASO is tested on a range of benchmark functions to verify its validity, qualitatively and quantitatively, and then applied to a hydrogeologic parameter estimation problem with success The results demonstrate that ASO is superior to some classic and newly emerging algorithms in the literature and is a promising solution to real-world engineering problems

Binary grasshopper optimisation algorithm approaches for feature selection problems

Abstract: Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature.

Iterated Greedy methods for the distributed permutation flowshop scheduling problem

Abstract: Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.

An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones

Abstract: Unmanned Aerial Vehicles, commonly known as drones, have attained considerable interest in recent years due to the potential of revolutionizing transport and logistics. Amazon were among the first to introduce the idea of using drones to deliver goods, followed by several other distribution companies working on similar services. The Traveling Salesman Problem, frequently used for planning last-mile delivery operations, can easily be modified to incorporate drones, resulting in a routing problem involving both the truck and aircraft. Introduced by Murray and Chu (2015) , the Flying Sidekick Traveling Salesman Problem considers a drone and truck collaborating. The drone can be launched and recovered at certain visits on the truck route, making it possible for both vehicles to deliver goods to customers in parallel. This generalization considerably decreases the operational cost of the routes, by reducing the total fuel consumption for the truck, as customers on the routes can be serviced by drones without covering additional miles for the trucks, and hence increase productivity. In this paper a mathematical model is formulated, defining a problem similar to the Flying Sidekick Traveling Salesman Problem, but for the capacitated multiple-truck case with time limit constraints and minimizing cost as objective function. The corresponding problem is denoted the Vehicle Routing Problem with Drones. Due to the difficulty of solving large instances to optimality, an Adaptive Large Neighborhood Search metaheuristic is proposed. Finally, extensive computational experiments are carried out. The tests investigate, among other things, how beneficial the inclusion of the drone-delivery option is compared to delivering all items using exclusively trucks. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.

Effective heuristics and metaheuristics to minimize total flowtime for the distributed permutation flowshop problem

Abstract: Distributed permutation flowshop scheduling problem (DPFSP) has become a very active research area in recent years. However, minimizing total flowtime in DPFSP, a very relevant and meaningful objective for today's dynamic manufacturing environment, has not captured much attention so far. In this paper, we address the DPFSP with total flowtime criterion. To suit the needs of different CPU time demands and solution quality, we present three constructive heuristics and four metaheuristics. The constructive heuristics are based on the well-known LR and NEH heuristics. The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems. We explore the problem-specific knowledge and accelerations to evaluate neighboring solutions for the considered problem. We introduce advanced and effective technologies like a referenced local search, a strategy to escape from local optima, and an enhanced intensive search method for the presented metaheuristics. A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out. The results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion.

Flexible Job-Shop Rescheduling for New Job Insertion by Using Discrete Jaya Algorithm

Abstract: Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.

Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection

Abstract: Selecting the most discriminative features is a challenging problem in many applications. Bio-inspired optimization algorithms have been widely applied to solve many optimization problems including the feature selection problem. In this paper, the most discriminating features were selected by a new Chaotic Dragonfly Algorithm (CDA) where chaotic maps embedded with searching iterations of the Dragonfly Algorithm (DA). Ten chaotic maps were employed to adjust the main parameters of dragonflies’ movements through the optimization process to accelerate the convergence rate and improve the efficiency of DA. The proposed algorithm is employed for selecting features from the dataset that were extracted from the Drug bank database, which contained 6712 drugs. In this paper, 553 drugs that were bio-transformed into liver are used. This data have four toxic effects, namely, irritant, mutagenic, reproductive, and tumorigenic effect, where each drug is represented by 31 chemical descriptors. The proposed model is mainly comprised of three phases; data pre-processing, features selection, and the classification phase. In the data pre-processing phase, Synthetic Minority Over-sampling Technique (SMOTE) was used to solve the problem of the imbalanced dataset. At the features selection phase, the most discriminating features were selected using CDA. Finally, the selected features from CDA were used to feed Support Vector Machine (SVM) classifier at the classification phase. Experimental results proved the capability of CDA to find the optimal feature subset, which maximizing the classification performance and minimizing the number of selected features compared with DA and the other meta-heuristic optimization algorithms. Moreover, the experiments showed that Gauss chaotic map was the appropriate map to significantly boost the performance of DA. Additionally, the high obtained value of accuracy (81.82–96.08%), recall (80.84–96.11%), precision (81.45–96.08%) and F-Score (81.14–96.1%) for all toxic effects proved the robustness of the proposed model.

Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Abstract: Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.

A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection

Abstract: Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the state-of-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems.

An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

Abstract: This paper proposes a hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the Water Cycle Algorithm to enhance its exploitation ability. In addition, to increase randomization in the new hybrid method, the streams in the Water Cycle Algorithm are allowed to update their position using a random walk (Levy flight). The random walk significantly improves the exploration ability of the Water Cycle Algorithm. The performance of the new hybrid Water Cycle–Moth-Flame Optimization algorithm (WCMFO) is investigated in 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of the WCMFO are compared to the other state-of-the-art metaheuristic algorithms. The results show that the hybrid method is able to outperform the other state-of-the-art metaheuristic algorithms in majority of the benchmark functions. To evaluate the efficiency of the WCMFO in solving complex constrained engineering and real-life problems, three well-known structural engineering problems are solved using WCMFO and the results are compared with the ones of the other metaheuristics in the literature. The results of the simulations revealed that the WCMFO is able to provide very competitive and promising results comparing to the other hybrid and metaheuristic algorithms.

A comprehensive review of krill herd algorithm: variants, hybrids and applications

Abstract: Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

Flower pollination algorithm: a comprehensive review

Abstract: Flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem . The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.

Sustainable closed-loop supply chain network design with discount supposition

Abstract: Supply chain network design (SCND) is one of the important, primary and strategic decisions affecting competitive advantages and all other decisions in supply chain management. Although most of papers in SCND focus only on the economic performance, this study considers simultaneously economic, social and environmental aspects. In this study, a new mixed integer nonlinear programming model is developed to formulate a multi-objective sustainable closed-loop supply chain network design problem by considering discount supposition in the transportation costs for the first time. In order to address the problem, not only traditional and recent metaheuristics are utilized, but also the algorithms are hybridized according to their strengths especially in intensification and diversification. To evaluate the efficiency and effectiveness of these algorithms, they are compared with each other by four assessment metrics for Pareto optimal analyses. Although the results indicate the performance of three proposed new hybridization algorithms, KAGA achieves better solutions compared with the others, but it needs more time. At the end, we introduced a real industrial example in glass industry to verify the proposed model and the algorithms.

The electric vehicle routing problem with energy consumption uncertainty

Abstract: Compared with conventional freight vehicles, electric freight vehicles create less local pollution and are thus generally perceived as a more sustainable means of goods distribution. In urban areas, such vehicles must often perform the entirety of their delivery routes without recharging. However, their energy consumption is subject to a fair amount of uncertainty, which is due to exogenous factors such as the weather and road conditions, endogenous factors such as driver behaviour, and several energy consumption parameters that are difficult to measure precisely. Hence we propose a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem. The objective is to determine minimum cost delivery routes capable of providing strong guarantees that a given vehicle will not run out of charge during its route. We formulate the problem as a robust mixed integer linear program and solve small instances to optimality using robust optimization techniques. Furthermore, we develop a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem, and we conduct several numerical tests to assess the quality of the methodology. The computational experiments illustrate the trade-off between cost and risk, and demonstrate the influence of several parameters on best found solutions. Furthermore, our heuristic identifies 42 new best solutions when tested on instances of the closely related robust capacitated vehicle routing problem.

Review on applications of particle swarm optimization in solar energy systems

Abstract: Solar energy is one of the most important factors used in the development of the countries. Since it is a renewable energy source, it reduces the demand on the non-renewable energy sources such as fossil fuels, oil, natural gas, nuclear, and other sources. Therefore, many researchers have sought to improve the performance of solar energy systems via applying several metaheuristic methods such as particle swarm optimization (PSO) which simulates the behavior of the fish schools or bird flocks. PSO has been used in different applications including engineering, manufacturing, and medicine. The main process of the PSO is to determine the optimal position for each particle inside the population. This is performed through updating the position using the velocity of each particle and the shared information between the particles. The aim of this paper is to provide a review on the PSO’s applications to improve the performance of solar energy systems and to identify the research gap for future work. The literature review used in this study indicates that the PSO is a very promising method to enhance the performance of solar energy systems.

On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Abstract: It is obvious from wider spectrum of successful applications that metaheuristic algorithms are potential solutions to hard optimization problems. Among such algorithms are swarm-based methods like particle swarm optimization and ant colony optimization which are increasingly attracting new researchers. Despite popularity, the core questions on performance issues are still partially answered due to limited insightful analyses. Mere investigation and comparison of end results may not reveal the reasons behind poor or better performance. This study, therefore, performed in-depth empirical analysis by quantitatively analyzing exploration and exploitation of five swarm-based metaheuristic algorithms. The analysis unearthed explanations the way algorithms performed on numerical problems as well as on real-world application of classification using adaptive neuro-fuzzy inference system (ANFIS) trained by selected metaheuristics. The outcome of empirical study suggested that coherence and consistency in the swarm individuals throughout iterations is the key to success in swarm-based metaheuristic algorithms. The analytical approach adopted in this study may be employed to perform component-wise diversity analysis so that the contribution of each component on performance may be determined for devising efficient search strategies.

jMetalPy: A Python framework for multi-objective optimization with metaheuristics

Abstract: This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a different programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation, constrained and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy offers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

Emperor Penguins Colony: a new metaheuristic algorithm for optimization

Abstract: A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

Abstract: The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions.