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This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool. [ABSTRACT FROM AUTHOR]

This paper presents a Chaos–Infused Moth–Flame Optimizer (CI-MFO). The parent algorithm is modified to account for deviations in search agent (moth) flight trajectory and variations in the flame orientation for an enhanced balance between exploration and exploitation tendencies. Actual photographic evidence showing light traces of such phototactic moths in-flight has been used to model flight path deviations using Chaos Theory. This approach considers their intelligence and erratic flight behavior (when subjected to excessive crowding). The performance of the developed CI-MFO algorithm is investigated comprehensively using a suite of fifty-eight benchmarking functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal, six CEC-2005 hybrid-composite, and twenty-nine CEC-2017 hybrid-composite functions. The proposed algorithm's effectiveness is tested against several classical algorithms and some modified metaheuristic optimization algorithms in terms of obtained mean optima and standard deviations, and scalability analysis is also performed. The paper concludes by solving several real-world problems and comparing the proposed algorithm's performance against several reported algorithms. The proposed algorithm exhibited a substantially better solution-finding ability. [ABSTRACT FROM AUTHOR]

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers are concentrating on a variety of metaheuristic (or evolutionary) algorithms and trying to suggest cutting-edge hybrid techniques to handle FS issues. The use of hybrid metaheuristic approaches for FS has thus been the subject of numerous research works. The purpose of this paper is to critically assess the existing hybrid FS approaches and to give a thorough literature review on the hybridization of different metaheuristic/evolutionary strategies that have been employed for supporting FS. This article reviews pertinent documents on hybrid frameworks that were published in the period from 2009 to 2022 and offers a thorough analysis of the used techniques, classifiers, datasets, applications, assessment metrics, and schemes of hybridization. Additionally, new open research issues and challenges are identified to pinpoint the areas that have to be further explored for additional study. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is a promising approach to the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger comfort, as well as lateral and longitudinal tracking, is challenging. Numerous tuning parameters and conflicting requirements need to be considered. In this paper, we formulate the MPC tuning task as a multi-objective optimization problem. Its solution is demanding for two reasons: First, MPC-parameterizations are evaluated in a computationally expensive simulation environment. As a result, the optimization algorithm needs to be as sample-efficient as possible. Second, for some poor parameterizations, the simulation cannot be completed; therefore, useful objective function values are not available (for instance, learning with crash constraints). In this work, we compare the sample efficiency of multi-objective particle swarm optimization (MOPSO), a genetic algorithm (NSGA-II), and multiple versions of Bayesian optimization (BO). We extend BO by introducing an adaptive batch size to limit the computational overhead. In addition, we devise a method to deal with crash constraints. The results show that BO works best for a small budget, NSGA-II is best for medium budgets, and none of the evaluated optimizers are superior to random search for large budgets. Both proposed BO extensions are, therefore, shown to be beneficial. [ABSTRACT FROM AUTHOR]

Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms. [ABSTRACT FROM AUTHOR]

Optimization is a very important issue to efficiently solve engineering problems. The clear comprehension of the elements and processes of optimizer algorithms is fundamental to ensure that engineer students are capable to apply, tune, and design new algorithms. This paper presents an educational platform based on LEGO© EV3 and MATLAB to assist the learning of the principles of classical and metaheuristic optimization algorithms at undergraduate level, by providing a simple and easy-to-follow teaching setup. The proposed study aims to accompany students through the learning of optimization fundamentals by building hands-on robotic experiments. The proposed educational platform has been successfully applied to several undergraduate courses within the Electronics Department at the University of Guadalajara. The description of each experiment and the evaluation of their impact in the student performance are both provided in the paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented. [ABSTRACT FROM AUTHOR]

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers' energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques. [ABSTRACT FROM AUTHOR]

The Whale Optimization Algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design. [ABSTRACT FROM AUTHOR]

The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications' task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing. [ABSTRACT FROM AUTHOR]

The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computing methodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledge-based methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men's Handball Championship, in order to estimate the weights of the indicators. Then, these weights were used to identify the best goalkeeper and identify and rank the top five goalkeepers as determined by the tournament organisers. The results obtained show that using the metaheuristic-based method is extremely helpful in quantifying the expert assessments, which are often challenging to express in a disaggregated form. The other two techniques offer a less optimal but more easily interpretable result for coaches and fans. [ABSTRACT FROM AUTHOR]

This paper describes a unified view of parallel evolutionary algorithms for multi-objective optimization problems. The parallel optimization algorithms are detailed from both design and implementation aspects. The proposed taxonomy is based on three hierarchical parallel models. Moreover, various parallel architectures are taken into account. The performance assessment issue of parallel multi-objective evolutionary algorithms (MOEA) is also presented. This work can be extended to any population-based metaheuristics such as particle swarm and scatter search. • The main highlights of this paper is the development of a unified view of parallel multi-objective evolutionary algorithms in terms of design and implementation. • The parallel models have been analyzed according to different parallel architectures. • The different parallel models have been illustrated. • The paper is also a guideline to researchers and engineers to design and implement efficient and effective parallel evolutionary algorithms for multi-objective optimization. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

The present paper deals with structural health monitoring of trusses, space frame and plate structure utilizing the Bayesian data fusion approach. The application of the proposed approach has been demonstrated on a 25-member plane truss, a 42-member space frame, a cantilever plate and a 120-member space truss. Different damage indexes of interest have been calculated for the damaged structure utilizing the natural frequency and modeshapes as damage indicators. Damage indexes used are modal strain energy (DIMSE), frequency response function strain energy dissipation ratio (FRFSEDR), flexibility strain energy damage ratio (FSEDR) and residual force-based damage index (RFBDI). Next, the Bayesian data fusion approach has been applied to these four damage indexes to find out the accurate damage location. The proposed approach reduces the number of suspected damaged elements in the structure significantly, thus reducing the computational time of optimization algorithm. Proposed algorithm has also shown encouraging performance in noisy environments. Overall present approach is found to be robust and computationally efficient, and thus can be applied for damage detection involving field evaluation of various structures. [ABSTRACT FROM AUTHOR]

A novel nature-inspired metaheuristic optimization algorithm, called the quantum firefly algorithm, is proposed in this paper. The algorithm imitates (a) the social behaviour of fireflies mating in nature, (b) laws of quantum physics, and (c) laws of natural evolution. The algorithm combines the powers of two well-known algorithms: the firefly algorithm and the quantum genetic algorithm. The proposed quantum firefly algorithm's performance is tested on 15 mathematical test functions and one structural design problem. The obtained results show that the quantum firefly algorithm is very competitive compared to the firefly algorithm and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]

This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm's accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption. [ABSTRACT FROM AUTHOR]

Thanks to the advent of powerful computers, searching for optimal solutions to engineering design problems becomes easier every day. Numerous researchers are still developing modern optimization algorithms, and the competition for "the most efficient optimization algorithm" continues apace. This study evaluates the performances of the Crow Search Algorithm (CSA) and a slightly modified variant (CSAM) in one of the most popular and controversial competitions in the structural optimization field for the first time. Unlike most of the works on structural optimization, this paper does not tell a success story. After days of computation to collect the sensitivity and convergence data, it is shown that both CSA and CSAM mostly fail compared to today's competitive algorithms. The findings of the study are discussed through tables and plots in detail to share the unfavorable experience on the truss optimization attempts, to review the difficulties of using parameter-controlled algorithms in structural optimization through CSA, and to save time for the researchers in the field. [ABSTRACT FROM AUTHOR]

Traditional seismic acquisition systems suffer from a number of difficulties related to telemetry cables that are used as a means of data transmission. Transforming the traditional seismic acquisition system to a wireless system has been considered as a potential solution to most of these difficulties. Wireless seismic acquisition systems have to serve a huge aggregate data rate requirement, as is usually the case in a large wireless sensor network. This paper considers a novel wireless acquisition system with a generalized tree topology, and studies the maximum achievable transmission data rates from the geophones to the wireless gateways, where successive interference cancellation decoding is used at the gateway nodes. We consider the problem of sum-rate maximization by optimizing the decoding set at each gateway, i.e. which group of geophones will be decoded at each gateway. Various integer programming algorithms are proposed for solving this maximization problem. These optimization algorithms are simulated and compared, where it is shown that the ant system algorithm achieves the highest sum-rate, with up to 82 % increase compared to the case of no optimization, and a relatively low computational complexity compared to other algorithms, e.g., it may take the ant system algorithm only 19 % of the time consumed by the angle-modulated particle swarm optimization. Furthermore, the data delivery from the gateways to the data center is also considered. In this stage, two cases, with different buffer sizes at the gateways, are studied. For small-size buffers, two optimization problems are identified and solved. The first problem considers the minimization of the total power of the gateways, and the second problem considers power fairness between the gateways. It is demonstrated that the first problem reduces the total power of the gateways by 43 % compared to the second problem. For large-size buffers, the problem of maximizing the weighted sum rate of the gateways is shown to be convex, and the globally optimal solution is obtained efficiently. [ABSTRACT FROM AUTHOR]

Optimized task scheduling is key to achieve high performance in the cluster-computing systems whose application is broad ranging from scientific to the military purposes. This combinatorial problem is NP-hard from the time complexity perspective, where applying newly proposed metaheuristics to it deserves further investigation based on the well-known no-free-lunch theorem. Accordingly, in this paper, an enhanced version of cuckoo optimization algorithm (COA) named E-COA is proposed to cope with the static task scheduling problem in the mesh topology cluster-computing environments. The proposed approach is equipped with an efficient adaptive semi-stochastic egg-laying strategy that significantly improves the local and global search potentiality of the basic COA. The experiments on a comprehensive set of randomly generated task graphs with different structural parameters reveal the efficiency of the proposed approach from the performance point of view, especially for the small-scale samples, and where the number of clusters in the machine is very restricted, i.e., we are in the lack of computational resource. [ABSTRACT FROM AUTHOR]

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns and . GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm is a novel metaheuristic algorithm that imitates the social behavior of humpback whales. In this algorithm, the bubble-net hunting strategy of humpback whales is exploited. However, this algorithm, in its present form, is appropriate for continuous problems. To make it applicable to discrete problems, a binary version of this algorithm is being proposed in this paper. In the proposed approach, the solutions are binarized and sigmoidal transfer function is utilized to update the position of whales. The performance of the proposed algorithm is evaluated on 29 benchmark functions. Furthermore, unpaired t test is carried out to illustrate its statistical significance. The experimental results depict that the proposed algorithm outperforms others in respect of benchmark test functions. The proposed approach is applied on electrical engineering problem, a real-life application, named as "unit commitment". The proposed approach uses the priority list to handle spinning reserve constraints and search mechanism to handle minimum up/down time constraints. It is tested on standard IEEE systems consisting of 4, 10, 20, 40, 80, and 100 units and on IEEE 118-bus system and Taiwan 38-bus system as well. Experimental results reveal that the proposed approach is superior to other algorithms in terms of lower production cost. [ABSTRACT FROM AUTHOR]

Cuckoo optimization algorithm (COA) is inspired from the special and exotic lifestyle of a bird family called the cuckoo and her amazing and unique behavior in egg laying and breeding. Just like any other population-based swarm intelligence metaheuristic algorithms, the basic COA starts with a set of randomly generated solutions called "habitats." Actually, the habitats can be the current locations of either the mature cuckoos or their eggs. In an iterative manner, cuckoos lay their eggs around their habitats inside the other birds' nests by mimicking their eggs' color, pattern, and size, and this is a kind of parasitic brooding behavior. Some hosts may discriminate the strange eggs and throw them out while the others not. The survival competition between cuckoos and their hosts, and migration of cuckoos in swarm are two main underlying motivations to introduce the COA. In this paper, an adaptive cuckoo optimization algorithm named A-COA is proposed in which three novelties in egg-laying and migration phases are applied. These modifications have made the basic algorithm more efficient with faster convergence to solve continuous and discrete optimization problems. A comprehensive comparison study of A-COA versus not only the basic COA but also some other conventional metaheuristics like GA, PSO, ABC, and TLBO has been made on a variety of unimodal and multimodal numerical benchmark functions with different characteristics, and the results show an overall 25.85% of improvement in terms of performance with a faster convergence speed compared to the basic COA, where the statistical Wilcoxon rank-sum test certifies our conclusions. In addition, a discretized version of A-COA and its application to the multiprocessor task scheduling problem as a complex combinatorial optimization problem are investigated where the proposed A-COA is very competitive with not only the strongest conventional heuristics, for example, MCP, ETF, and DLS, but also the basic COA and the newly proposed ACO-based approach. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the properties of the superior solution set search (SSSS) problem and point out the structural similarities between it and the multiobjective optimization problem. Based on the analyzed properties, we propose a new Firefly algorithm (FA) based on superior relations, which can search for the superior solution set by using the user's quantitative desire level as a search strategy. By introducing the distance of the problem space into the moving mechanism, FA makes it possible to search for multiple local optimal solutions by dividing the solution set into multiple groups. By analyzing this property of FA, we clarify the affinity between FA and the SSSS problem. We subsequently analyze the SSSS problem and FA, and discuss their properties together in a similar problem setting. We propose an FA based on superior relations as a new optimization technique for the superior solution set problem based on these analyses. Numerical experiments are then conducted using the SSSS problem and demonstrate the usefulness of the proposed method by comparing the performance of the proposed method with the conventional FA. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In Finite Element Method (FEM), the stress components are calculated within the elements firstly, and then these components are recovered to the nodes. For the recovery process, there are several well-known methods in which the increase of their accuracy imposes additional costs into the problem. In this paper, a new nodal stress recovery technique is proposed in which Colliding Bodies Optimization (CBO) Algorithm fits an appropriative function for nodal stress fields. The CBO employs this function to compute the stress components in the nodal coordinates. Therefore, a particular model to stress fields and its components will be available. It can be considered as a connection between analytical approaches and numerical methods, providing benefits of both categories. Finally, the accuracy, efficiency, and applicability of the new technique are investigated employing three diverse examples. [ABSTRACT FROM AUTHOR]

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems. • A novel strategy namely fast random opposition-based learning (FROBL) is proposed. • The FROBL is inspired by OBL and ROBL techniques to explore the search space efficiently. • FROBL is appended to the GJO algorithm to propose the FROBL-GJO algorithm. • FROBL-GJO is employed to solve different benchmark functions, and engineering problems. • FROBL-GJO generates good results compared to OBL-GJO, ROBL-GJO and other algorithms. [ABSTRACT FROM AUTHOR]

Recent developments in modelling and optimization approaches for the production of mineral and energy resources have resulted in new simultaneous stochastic optimization frameworks and related digital technologies. A mining complex is a type of value chain whereby raw materials (minerals) extracted from various mineral deposits are transformed into a set of sellable products, using the available processing streams. The supply of materials extracted from a group of mines represents a major source of uncertainty in mining operations and mineral value chains. The simultaneous stochastic optimization of mining complexes, presented herein, aims to address major limitations of past approaches by modelling and optimizing several interrelated aspects of the mineral value chain in a single model. This single optimization model integrates material extraction from a set of sources along with their uncertainty, the related risk management, blending, stockpiling, non-linear transformations that occur in the available processing streams, the utilization of processing streams, and, finally, the transportation of products to customers. Uncertainty in materials extracted from the related mineral deposits of a mining complex is represented by a group of stochastic simulations. This paper presents a two-stage stochastic mixed integer nonlinear programming formulation for modelling and optimizing a mining complex, along with a metaheuristic-based solver that facilitates the practical optimization of exceptionally large mathematical formulations. The distinct advantages of the approach presented herein are demonstrated through two case studies, where the stochastic framework is compared to past approaches that ignore uncertainty. Results demonstrate major improvements in both meeting forecasted production targets and net present value. Concepts and methods presented in this paper for the simultaneous stochastic optimization for mining complexes may be adopted and applied to the optimization of smart oil fields. [ABSTRACT FROM AUTHOR]

Low-level parallel programming is a tedious and error-prone task, especially when combining several programming models such as OpenMP, MPI, and CUDA. Algorithmic skeletons are a well-known high-level solution to these issues. They provide recurring building blocks such as map, fold, and zip, which are used by the application programmer and executed in parallel. In the present paper, we use the skeleton library Muesli in order to solve hard optimization problems by applying swarm intelligence (SI)-based metaheuristics. We investigate, how much hardware can reasonably be employed in order to find quickly a good solution using Fish School Search (FSS), which is a rather new and innovative SI-based metaheuristic. Moreover, we compare the implementation effort and performance of low-level and high-level implementations of FSS. [ABSTRACT FROM AUTHOR]

Abstract In complex socio-technical systems it is not easy to find a balance between the welfare state (i.e., a state where the overall performance of a system is optimal) and a situation in which individual components act selfishly to optimize their own utilities. This is even harder when individuals compete for scarce resources. In order to deal with this, some forms of biasing the optimization process have been proposed. However, mostly, such approaches only work for cooperative scenarios. When resources are scarce, the components of the system compete for them, thus approaches designed for cooperative systems are not necessarily appropriate. In the present paper an approach is proposed, which is based on a synergy between: (i) a global optimization process in which the system authority employs metaheuristics, and (ii) reinforcement learning processes that run at each component or agent. Both the agents and the system authority exchange solutions that are incorporated by the other party. The contributions of the proposed approach are twofold: a general scheme for such synergy is given and its benefits are shown in scenarios related to selfish routing, a typical load balance problem in a complex socio-technical system. Highlights • A synergy between metaheuristics and reinforcement learning is proposed. • Useful when aligning the social (system) and individuals optima. • An application to selfish routing (various traffic networks) is shown as illustration of the general approach. • Travel time in road networks reduced by biasing individuals' (agents') decision-making. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new mathematical model is presented, for the first time, for multi-item economic order quantity for growing items considering various operational constraints. The model aims to maximize company's total profit by determining the optimal values of the decision variables including: number of ordered items from each type and the time period needed to grow each type of items. To propose a realistic model which can be implemented in variety of real-world problems, different operational constraints are considered including: on-hand budget, warehouse capacity, and total allowable holding cost constraints. To solve the problem in small sizes a well-known exact solution methodology called Sequential Quadratic Programming is utilized. To solve the problem in medium and large sizes two novel hybrid metaheuristics called Sine Cosine Crow Search Algorithm and Water Cycle Moth–Flame Optimization algorithm are utilized due to sub-optimal results of the exact solution method. This is due to existence of enormous number of local optima and non-linearity of the proposed model. The performance of the algorithms is evaluated using different measures such as relative percentage deviation, percentage relative error, and CPU-Time. By solving various test problems in different sizes performance of the algorithms is evaluated and the best solution method for the problem is determined using statistical analyses. [ABSTRACT FROM AUTHOR]

Travelling Salesman Problem (TSP) is a problem in which the shortest possible route enabling a salesman to return to the starting point after visiting all the cities exactly once is determined. Travelling Salesman Problem is the basis for many problems ranging from vehicle routing to printed circuit boards assembly. This problem has been attracting great attention from researchers in the field of optimization; nevertheless it is difficult to solve TSP, especially for large-scale data sets. This paper presents a hybrid solution method based on Fluid Genetic Algorithm, Nearest Neighbor and 2-Opt methods for the solution of TSP. The performance of the proposed method is evaluated with the solution values of the Nearest Neighbor, Genetic Algorithm, Tabu Search, Ant Colony Optimization and the Tree Physiology Optimization algorithms in the literature. The solution results show the superiority of the proposed method in terms of solution time and quality. [ABSTRACT FROM AUTHOR]

In conventional single‐objective optimization, presenting multiple alternatives is difficult because the aim is to search for only one global optimal or suboptimal solution. In this paper, we propose a superior solution set search problem as an optimization problem to simultaneously find multiple excellent solutions in multimodal functions. In addition, we analyze the characteristics of the Firefly Algorithm (FA), which divides the search process into multiple clusters, and using this property, we efficiently search for the superior solution set. We interpret the metaheuristics strategy (i.e., diversification and intensification) of the superior solution set search problem and clarify the effects of parameters on the cluster search dynamics of FA in terms of the metaheuristics strategy. After the above analysis, we propose an adaptive FA that preliminarily performs parameter adjustment according to set target value schedule, evaluate indicators of diversification and intensification for the superior solution set search problem, and verify their usefulness by conducting a numerical experiment using three typical benchmark functions. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The Max-Sum diversity and the Max-Min diversity are two well-known optimization models to capture the notion of selecting a subset of diverse points from a given set. The resolution of their associated optimization problems provides solutions of different structures, in both cases with desirable characteristics. They have been extensively studied and we can find many metaheuristic methodologies, such as Greedy Randomized Adaptive Search Procedure, Tabu Search, Iterated Greedy, Variable Neighborhood Search, and Genetic algorithms applied to them to obtain high quality solutions. In this paper we solve the bi-objective problem in which both models are simultaneously optimized. No previous effort has been devoted to study the “combined problem” from a multi-objective perspective. In particular, we adapt the mono-objective methodologies applied to this problem to the resolution of the bi-objective problem, obtaining approximations to its efficient front. An empirical comparison discloses the best alternative to tackle this NP -hard problem. [ABSTRACT FROM AUTHOR]

In this paper, we address a variant of a classical optimization model in the context of maximizing the diversity of a set of elements. In particular, we propose heuristics to maximize the mean dispersion of the selected elements in a given set. This NP-hard problem was recently introduced as the maximum mean dispersion problem (MaxMeanDP), and it models several real problems, from pollution control to ranking of web pages. In this paper, we first review the previous methods for the MaxMeanDP, and then explore different tabu search approaches, and their influence on the quality of the solutions obtained. As a result, we propose a dynamic tabu search algorithm, based on three different neighborhoods. Experiments on previously reported instances show that the proposed procedure outperforms existing methods in terms of solution quality. It must be noted that our findings on the use of different memory structures invite to further consideration of the interplay between short and long term memory to enhance simple forms of tabu search. [ABSTRACT FROM AUTHOR]

• Sensors should be installed in strategic places for a reliable grid supervision. • Data criticalities refer to impending unobservability and incapacity of the state estimation functionality to handle bad data. • Metaheuristics can successfully tackle meter placement problems. In this paper, strategic observation of power grids refers to the optimized placement of measurements necessary to obtain the most likely operating condition of the grid, achieved in real-time monitoring via the state estimation (SE) functionality. The measuring system designed for SE considers the types, placement at various grid buses, and the total available measurements, which is crucial for reliable monitoring. The objective to be reached is to find the best places to install additional measuring units, in a limited number divided into pre-specified lots, to reduce data criticalities—whose importance is related to impending unobservability and incapacity of SE residual analysis to handle bad data. The process of finding an optimized solution for the meter placement problem is typically combinatorial and of considerable size, which makes metaheuristics the realistic option to solve the issue at hand. Metaheuristics are approximate optimization techniques with a pervasive success story in tackling various complex, real-world combinatorial optimization problems. This paper proposes the combined implementation of greedy randomized adaptive search procedure (GRASP), variable neighborhood descent (VND), and variable neighborhood search (VNS) optimization techniques. Numerical results performed on benchmark power grids (IEEE 30- and 118-bus) illustrate the application of the proposed approach and evince that high-quality solutions are achieved (validated by the exhaustive search results) in a computing time adequate for power grid operation planning studies. [ABSTRACT FROM AUTHOR]

An industrial problem encountered in steel mills is considered in the paper. The problem is formulated as a single-processor scheduling problem with a dynamic model of the task release date. In this model, a task is released for processing when its preprocessing is in the final state. The instantaneous rate of the change of the preprocessing state (from the initial to the final one) depends on the amount of resource allotted to the task. The resource available for task preprocessing is renewable and upper bounded. The problem consists in establishing the task sequence and the resource allocation that minimize the makespan. This problem has already been considered in the literature, but its complexity has not been formally proved. The proof of NP-hardness by polynomial reduction from the partition problem is presented in this paper. Two metaheuristics (simulated annealing and tabu search) are proposed to solve the problem. Both are tested, and the results are analyzed. [ABSTRACT FROM AUTHOR]

Determining the optimum placement of braces in steel frames has always been one of the most challenging issues in structural engineering. In this paper, the size and placement of the X-braces in planar frame structures is determined in a way that the total weight of the braced frames becomes minimum, while satisfying the design requirements and constraints. Variables of the optimization contain the cross sections for beams, columns, and X-braces as well as the placement of these braces in the frames. Attempt has also been made to consider all the constraints of an actual design problem. One of the other objectives of this study is to investigate the effect of including or excluding some of the constraints affecting the optimization of the planar frame design. For this purpose, the Colliding Bodies Optimization (CBO) and CBO-MDM algorithms have been utilized. Modified Dolphin Monitoring (MDM) operator is recently developed for improving the performance of the metaheuristic algorithms. Here, this operator is utilized to enhance the performance of the CBO algorithm to optimize the weight of the frames. For additional comparison of the results, the particle swarm optimization (PSO) algorithm and imperialist competitive algorithm (ICA) are used. [ABSTRACT FROM AUTHOR]

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. [ABSTRACT FROM AUTHOR]

In this paper, an improved imperialistic competitive algorithm is presented for real-valued optimization problems. A new method is introduced for the movement of colonies towards their imperialist, which is called assimilation. The proposed method uses Euclidean distance along with Pearson correlation coefficient as an operator for assimilating colonies with respect to their imperialists. Applications of the proposed algorithm to classical and recently published hard benchmark problems, and statistical analysis associated with the corresponding experimental results illustrated that the achieved success is significantly better than a number of state-of-the art methods. [ABSTRACT FROM AUTHOR]

With the simulation and optimization of a manufacturing system, the input parameters are sought to obtain the best performance of the system, considering in many cases a single objective or measure of performance. However, many decisions involve multiple criteria, usually in conflict. Currently, companies are required to improve their profitability as well as to reduce their impact on the environment and use energy rationally. In this paper, the wood products manufacturing process of a small company was studied to determine redesign alternatives, with simultaneous economic and environmental criteria. A simulation model of the system was built, which was validated and verified, and an optimization model for it in which two objectives were combined in a single function, incorporating in it the preferences of the decision makers with respect to each criterion. Results expose that combining both criteria it is possible to achieve substantial improvements in the benefit, at the same time that generating less emissions than if only the economic objective were pursued. It is concluded that simulation and optimization combining both criteria can improve economic-environmental performance in wood manufacturing systems, even in small companies. [ABSTRACT FROM AUTHOR]

Multi-objective design and operation optimization of energy supply systems with respect to costs, energy efficiency and environmental indicators is a very important, yet difficult and challenging task. The assumption of cost-optimal operation may impose the necessity for bi-level problems. This paper presents a methodology to formulate and solve multi-objective bi-level optimization problems where upper-level decision makers decide on design and policy, while plant operation is defined at the lower level. It can be used to assess energy consumption or emission reduction potential corresponding to cost-optimal operation. The methodology applies conceptual decomposition to the outer design- and inner operation-optimization problems. The first is solved with the scatter search method and the second using mixed integer linear programming. Scatter search is a metaheuristic method convenient to integrate problem-specific heuristics into the process and systematically take advantage of the designer's knowledge. The fuzzy approach considers multiple design objectives and helps formulate heuristic rules based on the decision criteria satisfaction rates. The case study illustrates the design of a trigeneration system for an urban settlement. The design objectives are: total costs, primary energy and greenhouse gases emission. Cost-optimal operation is presumed. The optimal plant design and operation are realistic and mostly consistent with the literature. Compared to the cost-optimal solution, other objectives might lead to reduced primary energy consumption for 7.21% or lower emission for 91.85% with 43.35% and 83.1% larger costs. The results are compared with the ones obtained with customary methods. It is demonstrated that the methodology is applicable and convenient for the considered class of problems. [ABSTRACT FROM AUTHOR]

There is a growing research interest on the application of evolutionary computation-based techniques in manufacturing optimization due to the fact that this field is associated with a plethora of complex combinatorial optimization problems. Differential evolution (DE), one of the latest developed evolutionary algorithms, has rarely been applied on manufacturing optimization problems (MOPs). A possible reason for the absence of DE from this research field is that DE was introduced as global optimizer over continuous spaces, while most of MOPs are of combinatorial nature with discrete decision variables. DE maintains and evolves floating-point vectors and therefore its application to MOPs that have solutions represented by permutations is not straightforward. This paper investigates the use of DE for the solution of the simple assembly line balancing problem (SALBP), a well-knownNP-hard MOP. Two basic formulation types for SALBP are examined, namely type-1 and type-2: the former attempts to minimize the number of workstations required to manufacture a product in an assembly line for a given fixed cycle time; while the latter attempts to minimize the cycle time of the line for a given number of stations. Extensive experiments carried out over public benchmarks test instances estimate the performance of DE approach. [ABSTRACT FROM PUBLISHER]