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Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

Pharmaceutical waste management is a significant concern that poses risks to human and environmental health. The ineffective management of expired and unused medications can harm individuals and communities. This study proposes a novel approach to address the issue of pharmaceutical waste management by developing a location-routing problem (LRP) model using mixed-integer linear programing (MILP) to optimize the collection and disposal of pharmaceutical waste. The proposed model aims to minimize transportation costs, construction of collection centers, disposal costs, and carbon dioxide emissions, making it a cost-effective and environmentally sustainable approach to managing pharmaceutical waste. Initially, the feasibility, validity, and efficiency of the proposed model are examined by solving the problem in the GAMS software using CPLEX solver for small-scale problems. Sensitivity analyses are conducted to ensure the accuracy, reliability, robustness, and usefulness of the mathematical model for decision-making. In view of the inherent computational complexity of the proposed model, which is classified as nondeterministic polynomial time-hard and poses considerable difficulties when exact solutions are sought for large-scale problems, the present study resorts to two metaheuristic algorithms, specifically particle swarm optimization (PSO) and genetic algorithm (GA), as a means minimizing the computational burden. The results indicate that GA outperforms PSO in terms of objective function and solution time, with an average improvement of approximately 1% and 20%, respectively. The proposed model and algorithms provide a comprehensive approach to addressing the critical issue of pharmaceutical waste management, benefiting the healthcare industry, and society as a whole. [ABSTRACT FROM AUTHOR]

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) have been recently gaining traction for many applications in monitoring and surveillance systems in the physical world specifically in agriculture, healthcare, and smart cities. Many clustering and routing approaches have been introduced to reduce the consumption of energy in WSNs to increase the lifetime of the network. In this study, we propose an improved version of grey wolf optimizer (GWO), a nature-inspired metaheuristic optimization algorithm, to perform cluster head selection and routing in WSN while maximizing the lifetime of WSN. GWO has a propensity to converge to local optima. To overcome this drawback of the conventional GWO, we introduce a balancing factor between the exploration and exploitation phases of the algorithm in addition to a mapping scheme. Comparative simulation and analysis of the proposed algorithm show significant improvement compared to frequently used and well-known approaches namely LEACH and PSO. [ABSTRACT FROM AUTHOR]

In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs. [ABSTRACT FROM AUTHOR]

In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses. [ABSTRACT FROM AUTHOR]

This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this paper, an updated review of scatter search (SS) is given. SS has been successfully applied in a variety of research problems, namely, data mining, bioinformatics, and engineering design. This paper presents the modified and hybrid versions of SS with their applications. The control strategies are discussed to show their impact on the performance of SS. various issues and future directions related to SS are also discussed. It inspires innovative researchers to use this algorithm for their research domains. [ABSTRACT FROM AUTHOR]

BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms. [ABSTRACT FROM AUTHOR]

With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor's expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems. [ABSTRACT FROM AUTHOR]

Compliant mechanisms are crucial parts in precise engineering but modeling techniques are restricted by a high complexity of their mechanical behaviors. Therefore, this paper devotes an optimal design method for compliant mechanisms. The integration method is a hybridization of statistics, finite element method, artificial intelligence, and metaheuristics. In order to demonstrate the superiority of the method, one degree of freedom is considered as a study object. Firstly, numerical datasets are achieved by the finite element method. Subsequently, the main design parameters of the mechanism are identified via analysis of variance. Desirability of both displacement and frequency of the mechanism is determined, and then, they are embedded inside a fuzzy logic system to combine into a single fitness function. Then, the relationship between the fine design variables and the fitness function is modeled using the adaptive network-based fuzzy inference system. Next, the single fitness function is maximized via moth-flame optimization algorithm. The optimal results determined that the frequency is 79.517 Hz and displacement is 1.897 mm. In terms of determining the global optimum solution, the current method is compared with the Taguchi, desirability, and Taguchi-integrated fuzzy methods. The results showed that the current method is better than those methods. Additionally, the devoted method outperforms the other metaheuristic algorithms such as TLBO, Jaya, PSOGSA, SCA, ALO, and LAPO in terms of faster convergence. The result of this study will be considered to apply for multiple-degrees-of-freedom compliant mechanisms in future work. [ABSTRACT FROM AUTHOR]

Seagull optimization algorithm (SOA) inspired by the migration and attack behavior of seagulls in nature is used to solve the global optimization problem. However, like other well-known metaheuristic algorithms, SOA has low computational accuracy and premature convergence. Therefore, in the current work, these problems are solved by proposing the modified version of SOA. This paper proposes a novel hybrid algorithm, called whale optimization with seagull algorithm (WSOA), for solving global optimization problems. The main reason is that the spiral attack prey of seagulls is very similar to the predation behavior of whale bubble net, and the WOA has strong global search ability. Therefore, firstly, this paper combines WOA's contraction surrounding mechanism with SOA's spiral attack behavior to improve the calculation accuracy of SOA. Secondly, the levy flight strategy is introduced into the search formula of SOA, which can effectively avoid premature convergence of algorithms and balance exploration and exploitation among algorithms more effectively. In order to evaluate the effectiveness of solving global optimization problems, 25 benchmark test functions are tested, and WSOA is compared with seven famous metaheuristic algorithms. Statistical analysis and results comparison show that WSOA has obvious advantages compared with other algorithms. Finally, four engineering examples are tested with the proposed algorithm, and the effectiveness and feasibility of WSOA are verified. [ABSTRACT FROM AUTHOR]

Selective harmonics elimination (SHE) is a widely applied control strategy in multilvel inverters for harmonics reduction. SHE is designed for the elimination of low-order harmonics while keeping the fundamental component equal to any previously specified amplitude. This paper proposes a novel bio-inspired metaheuristic optimization algorithm called Black Widow Optimization Algorithm (BWOA) for solving the SHE set of equations. BWOA mimics the spiders' different movement strategies for courtship-mating, guaranteeing the exploration and exploitation of the search space. The optimization results show the reliability of BWOA compared to the state-of-the-art metaheuristic algorithms and show competitive results as a microalgorithm, opening its future application for an on-line optimization calculation in low requirement hardware. [ABSTRACT FROM AUTHOR]

Clustering analysis is an important and difficult task in data mining and big data analysis. Although being a widely used clustering analysis technique, variable clustering did not get enough attention in previous studies. Inspired by the metaheuristic optimization techniques developed for clustering data items, we try to overcome the main shortcoming of k-means-based variable clustering algorithm, which is being sensitive to initial centroids by introducing the metaheuristic optimization. A novel memetic algorithm named MCLPSO (Memetic Comprehensive Learning Particle Swarm Optimization) based on CLPSO (Comprehensive Learning Particle Swarm Optimization) has been studied under the framework of memetic computing in our previous work. In this work, MCLPSO is used as a metaheuristic approach to improve the k-means-based variable clustering algorithm by adjusting the initial centroids iteratively to maximize the homogeneity of the clustering results. In MCLPSO, a chaotic local search operator is used and a simulated annealing- (SA-) based local search strategy is developed by combining the cognition-only PSO model with SA. The adaptive memetic strategy can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the variable clustering criterion on several datasets compared with the original variable clustering method. Finally, for practical use, we also developed a web-based interactive software platform for the proposed approach and give a practical case study—analyzing the performance of semiconductor manufacturing system to demonstrate the usage. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics. [ABSTRACT FROM AUTHOR]

The Interior Epigraph Directions (IED) method for solving constrained nonsmooth and nonconvex optimization problem via Generalized Augmented Lagrangian Duality considers the dual problem induced by a Generalized Augmented Lagrangian Duality scheme and obtains the primal solution by generating a sequence of iterates in the interior of the epigraph of the dual function. In this approach, the value of the dual function at some point in the dual space is given by minimizing the Lagrangian. The first version of the IED method uses the Matlab routine fminsearch for this minimization. The second version uses NFDNA, a tailored algorithm for unconstrained, nonsmooth and nonconvex problems. However, the results obtained with fminsearch and NFDNA were not satisfactory. The current version of the IED method, presented in this work, employs a Genetic Algorithm, which is free of any strategy to handle the constraints, a difficult task when a metaheuristic, such as GA, is applied alone to solve constrained optimization problems. Two sets of constrained optimization problems from mathematics and mechanical engineering were solved and compared with literature. It is shown that the proposed hybrid algorithm is able to solve problems where fminsearch and NFDNA fail. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to improve installation of friction dampers in civil structures subjected to artificial and real earthquakes using a metaheuristic optimization technique. The Firefly Algorithm is used in this work and it is linked with a computational routine based on Finite Differences Method to solve simultaneous optimization of friction dampers problem. The methodology is implemented for two kinds of structures: a 2D steel frame building and a 3D concrete frame building. The scope of this study is to reduce two different objective functions: (i) the maximum displacement at the top of a structure and (ii) maximum interstorey drift. The results showed that the methodology was able to reduce two objective functions and it can be recommended as an efficient tool to project optimal fiction dampers. [ABSTRACT FROM AUTHOR]

A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics. [ABSTRACT FROM AUTHOR]

Water wave optimization (WWO) is a novel metaheuristic method that is based on shallow water wave theory, which has simple structure, easy realization, and good performance even with a small population. To improve the convergence speed and calculation precision even further, this paper on elite opposition-based strategy water wave optimization (EOBWWO) is proposed, and it has been applied for function optimization and structure engineering design problems. There are three major optimization strategies in the improvement: elite opposition-based (EOB) learning strategy enhances the diversity of population, local neighborhood search strategy is introduced to enhance local search in breaking operation, and improved propagation operator provides the improved algorithm with a better balance between exploration and exploitation. EOBWWO algorithm is verified by using 20 benchmark functions and two structure engineering design problems and the performance of EOBWWO is compared against those of the state-of-the-art algorithms. Experimental results show that the proposed algorithm has faster convergence speed, higher calculation precision, with the exact solution being even obtained on some benchmark functions, and a higher degree of stability than other comparative algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach for solving a real two-dimensional irregular cutting stock problem in ship building industry. Cutting stock problem is a common cutting and packing problem that arises in a variety of industrial applications. A modification of selection heuristic Exact Fit is applied in our research. In the case referring to irregular shapes, a placement heuristics is more important to construct a complete solution. A placement heuristic relating to bottom-left-fill is presented. We evaluate the proposed approach using generated instance only with convex shapes in literatures and some instances with nonconvex shapes based on real problem from ship building industry. The results demonstrate that the effectiveness and efficiency of the proposed approach are significantly better than some conventional heuristics. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators). Three types of mutation operators (uniform, nonuniform, and polynomial) were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimization algorithm for global numerical optimization. The optimal quality, convergence speed, and statistical hypothesis testing of our algorithm are superior to those previous high performance algorithms such as RCCRO, HP-CRO2, and OCRO. [ABSTRACT FROM AUTHOR]

Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method. [ABSTRACT FROM AUTHOR]

The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library. [ABSTRACT FROM AUTHOR]

Economic load dispatch problem is a popular optimization problem in electrical power system field, which has been so far tackled by various mathematical and metaheuristic approaches including Lagrangian relaxation, branch and bound method, genetic algorithm, tabu search, particle swarm optimization, harmony search, and Taguchi method. On top of these techniques, this study proposes a novelmemetic algorithmscheme combining metaheuristic algorithmand gradient-based technique to find better solutions for an economic load dispatch problem with valve-point loading. Because metaheuristic algorithms have the strength in global search and gradient-based techniques have the strength in local search, the combination approach obtains better results than those of any single approach. A bench-mark example of 40 generating-unit economic load dispatch problem demonstrates that the memetic approach can further improve the existing best solutions from the literature. [ABSTRACT FROM AUTHOR]

To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS). The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG) in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application. [ABSTRACT FROM AUTHOR]

This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axis wind turbines (WT). The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate of the sectional blade aerodynamics. The framework considers an innovative nested hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize the aerodynamic efficiency of small WT (SWT) rotors for a wide range of operational conditions. The design variables are (1) the airfoil shape at the different blade span positions and the radial variation of the geometrical variables of (2) chord length, (3) twist angle, and (4) thickness along the blade span. A wind tunnel validation study of optimized rotors based on the NACA 4-digit airfoil series is presented. Based on the experimental data, improvements in terms of the aerodynamic efficiency, the cut-in wind speed, and the amount of material used during the manufacturing process were achieved. Recommendations for the aerodynamic design of SWT rotors are provided based on field experience. [ABSTRACT FROM AUTHOR]

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu's method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various metaheuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1,ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniqueswhen all of themwere applied tomultilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm. [ABSTRACT FROM AUTHOR]

Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller subsets. All items in S must be contained in one and only one partition. Related problems are set packing (all itemsmust be contained in zero or one partitions) and set covering (all itemsmust be contained in at least one partition).Here, we present a hybrid solver based on ant colony optimization (ACO) combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase. [ABSTRACT FROM AUTHOR]

To improve the performance of the krill herd (KH) algorithm, in this paper, a L'evy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local L'evy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimizationmethods. Especially, this newmethod can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH. [ABSTRACT FROM AUTHOR]

Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. InHSTL algorithm, four strategies (harmonymemory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems. [ABSTRACT FROM AUTHOR]

Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employingConwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with amulti-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithmand simulated annealing) and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics. [ABSTRACT FROM AUTHOR]

The type of mathematical modeling selected for the optimum design problems of steel skeletal frames affects the size and mathematical complexity of the programming problem obtained. Survey on the structural optimization literature reveals that there are basically two types of design optimization formulation. In the first type only cross sectional properties of frame members are taken as design variables. In such formulation when the values of design variables change during design cycles, it becomes necessary to analyze the structure and update the response of steel frame to the external loading. Structural analysis in this type is a complementary part of the design process. In the second type joint coordinates are also treated as design variables in addition to the cross sectional properties of members. Such formulation eliminates the necessity of carrying out structural analysis in every design cycle. The values of the joint displacements are determined by the optimization techniques in addition to cross sectional properties. The structural optimization literature contains structural design algorithms that make use of both type of formulation. In this study a review is carried out on mathematical and metaheuristic algorithms where the effect of the mathematical modeling on the efficiency of these algorithms is discussed. [ABSTRACT FROM AUTHOR]

Recently, a new fruit fly optimization algorithm (FOA) is proposed to solve optimization problems. In this paper, we empirically study the performance of FOA. Six different nonlinear functions are selected as testing functions. The experimental results illustrate that FOA cannot solve complex optimization problems effectively. In order to enhance the performance of FOA, an improved FOA (named LGMS-FOA) is proposed. Simulation results and comparisons of LGMS-FOA with FOA and other metaheuristics show that LGMS-FOA can greatly enhance the searching efficiency and greatly improve the searching quality. [ABSTRACT FROM AUTHOR]