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Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and assess the approach, a case study was conducted in Tehran, the capital of Iran. To satisfy the first step, four conflicting objective functions including maximization of consistency, maximization of dependency, maximization of suitability and maximization of compactness were defined and optimized using non-dominated sorting genetic algorithm. Per-capita demand was also employed as a constraint in the optimization process. Clustering analysis based on ant colony optimization was used to satisfy the second step. The results of the optimization were satisfactory both from a convergence and from a repeatability point of view. Furthermore, the objective functions of optimized arrangements were better than existing land-use arrangement in the area, with the per-capita demand deficiency significantly compensated. The approach was also communicated to urban planners in order to assess its usefulness. In conclusion, the proposed approach can extensively support and facilitate decision making of urban planners and policy makers in reforming and updating existing detailed plans after land-use changes. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in selection mechanisms based on the hypervolume indicator with a particular emphasis on the mechanism used in an improved version of the S metric selection evolutionary multi-objective algorithm (SMS-EMOA) called iSMS-EMOA, which exploits the locality property of the hypervolume. Here, we propose a new selection scheme which approximates the contribution of solutions to the hypervolume and it is designed to preserve the locality property exploited by iSMS-EMOA. This approach is proposed as an alternative to the use of exact hypervolume calculations and is aimed for solving many-objective optimization problems. The proposed approach is called 'approximate version of the improved SMS-EMOA (aviSMS-EMOA)' and is validated using standard test problems (with three or more objectives) and performance indicators taken from the specialized literature. Our preliminary results indicate that our proposed approach is a good alternative to solve many-objective optimization problems, if we consider both quality in the solutions and running time required to obtain them because it outperforms two versions of the original SMS-EMOA that approximate the contributions to the hypervolume, it outperforms MOEA/D using penalty boundary intersection and it is competitive with respect to the original SMS-EMOA in several of the test problems adopted. Also, its computational cost is reasonable (it is slower than MOEA/D, but faster than SMS-EMOA). [ABSTRACT FROM AUTHOR]

The teaching learning-based optimization (TLBO) is a population-based optimization algorithm suitable for solving complex problems. TLBO imitates the interaction between a teacher and her/his students. The global solution search process of this approach consists of two phases: the teacher- and the learner-phase. This paper proposes a multi-objective teaching learning algorithm based on decomposition (MOTLA/D) for solving a reactive power handling problem. The proposed method is validated on three test systems, and it is compared with respect to a state-of-the-art multi-objective evolutionary algorithm based on decomposition (MOEA/D). [ABSTRACT FROM PUBLISHER]

This article introduces the circuit design problem as a synthesis procedure. An evolutionary technique denominated Genetic Programing (GP) is proposed as the main engine for the synthesis of logic circuits. This article argues that the synthesis of circuits using bottom-up procedures (such as GP) is at least as powerful as any top-down method, and that this is possible by means of the replication of a single element: the binary multiplexer. The properties of this device are described as a sound basis for the synthesis of logic circuits using GP. Several circuits are synthesized and contrasted against two design methods: the standard implementation of logic functions using multiplexers, and ordered binary decision diagrams. [ABSTRACT FROM AUTHOR]

This paper introduces a cultural algorithm that uses domain knowledge to improve the performance of an evolutionary programming technique adopted for constrained optimization. The proposed approach extracts domain knowledge during the evolutionary process and builds a map of the feasible region to guide the search more efficiently. Additionally, in order to have a more efficient memory management scheme, the current implementation uses 2n -trees to store this map of the feasible region. Results indicate that the approach is able to produce very competitive results with respect to other optimization techniques at a considerably lower computational cost. [ABSTRACT FROM AUTHOR]

In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems. [Copyright &y& Elsevier]

After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, future trends in this area and some possible paths for further research are also addressed. [ABSTRACT FROM AUTHOR]

Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We propose a new methodology for structure and parameter tuning of Takagi–Sugeno–Kang fuzzy models using several optimization techniques. The output parameters are determined analytically, by finding the minimum of the root-mean-square error (RMSE) for a properly defined error function. The membership functions are simplified by considering symmetry and equispacing, to reduce the optimization problem of finding their parameters, and allow it to be carried out using the numerical method of gradient descent. Both algorithms are fast enough to finally implement a strategy based on the hill climbing approach to finding the optimal structure (number and type of membership functions) of the fuzzy system. The effectiveness of the proposed strategy is shown by comparing its performance, using four case studies found in current relevant works, to the popular adaptive network-based fuzzy inference system (ANFIS), and to other recently published strategies based on evolutionary fuzzy models. In terms of the RMSE, performance was at least 28% better in all case studies. [ABSTRACT FROM AUTHOR]

Although researchers have successfully incorporated metamodels in evolutionary algorithms to solve computational-expensive optimization problems, they have scarcely performed comparisons among different metamodeling techniques. This paper presents an in-depth comparison study over four of the most popular metamodeling techniques: polynomial response surface, Kriging, radial basis function neural network (RBF), and support vector regression. We adopted six well-known scalable test functions and performed experiments to evaluate their suitability to be coupled with an evolutionary algorithm and the appropriateness to surrogate problems by regions (instead of surrogating the entire problem). Notwithstanding that most researchers have undertaken accuracy as the main measure to discern among metamodels, this paper shows that the precision, measured with the ranking preservation indicator, gives a more valuable information for selecting purposes. Additionally, nonetheless each model has its own peculiarities; our results concur that RBF fulfills most of our interests. Furthermore, the readers can also benefit from this study if their problem at hand has certain characteristics such as a low budget of computational time or a low-dimension problem since they can assess specific results of our experimentation. [ABSTRACT FROM AUTHOR]

k-NN is one of the most popular and effective classifiers nowadays. However, it has some limitations that overcome its applicability in large scale scenarios: basically, it requires storing the whole training set, and it computes distances of a test sample with the training data set. These limitations have been traditionally alleviated with data reduction techniques. This paper introduces a multi-objective evolutionary approach for data reduction. Our method simultaneously generates prototypes and selects features for k-NN classifiers. Contrary to most of the existing approaches, our method treats the problem with multi-objective evolutionary optimizers. We show the effectiveness of our proposal in benchmark data and compare its performance with state of the art techniques. [ABSTRACT FROM AUTHOR]

Classification is a mainstream within the machine learning community. As a result, a large number of learning algorithms have been proposed. The performance of many of these could highly depend on the chosen values of their hyper-parameters. This paper introduces a novel method for addressing the model selection problem for a given classification task. In our model selection formulation, both the learning algorithm and its hyper-parameters are considered. In our proposed approach, model selection is tackled as a multi-objective optimization problem. The empirical error, or training error, and the model complexity are defined as the objectives. We adopt a multi-objective evolutionary algorithm as the search engine, due to its high performance and its advantages for solving multi-objective problems. The model complexity is estimated experimentally, in a general fashion, for any learning algorithm, through the VC dimension. Strategies for choosing a single model or for constructing an ensemble of models from the resulting non-dominated set are also proposed. Experimental results on benchmark data sets indicate the effectiveness of the proposed approach. Furthermore, a comparative study shows that the obtained models are highly competitive, in terms of generalization performance, with other methods in the state of the art that focus on a single-learning algorithm, or a single-objective approach. [ABSTRACT FROM AUTHOR]

Due to the many objectives and constraints involved in urban land use planning (ULUP), this is considered as a many‐objective and complex optimization problem that needs a variety of geographical analyses. In this article, the main target is improving NSGA‐III as an advanced many‐objective optimization algorithm for solving the ULUP problem. In this study, five objective functions (i.e., consistency, dependency, compactness, suitability, and per capita violation of land uses) are considered for their simultaneous optimization for allocation. The proposed algorithm is tested using the spatial data of region 7, district 1 of Tehran using a vector format. To evaluate the results, two more real datasets were implemented. The performance of the improved algorithm is compared concerning NSGA‐II and NSGA‐III in the main case study area and two other instances. The comparison results show that the improved algorithm increases the convergence and diversity of the generated solutions in ULUP concerning the results obtained by these two other algorithms. The results of the optimization with these methods can help decision‐makers toward sustainable development in the construction of new cities, new towns, and smart cities. [ABSTRACT FROM AUTHOR]

Abstract: This article presents multi-objective variants of two popular metaheuristics, namely, the artificial bee colony algorithm (ABC) and the teaching learning based optimization algorithm (TLBO). Both of them are used to solve an optimal power flow problem. The proposed multi-objective variants are based on a decomposition approach, where the multi-objective optimization problem is decomposed into a number of scalar optimization sub-problems which are simultaneously optimized. The proposed algorithms are tested on the IEEE 30-bus system with different objectives. In addition, an algorithm based on fuzzy set theory is used to select the best committed solution. The proposed approaches are compared with others metaheuristic algorithms available in the specialized literature. Results indicate that the proposed approaches are highly competitive and also able to generate a well-distributed set of non-dominated solutions for the optimal power flow problem. [Copyright &y& Elsevier]

Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar subproblems, which are then optimized in a collaborative manner. However, when tackling imbalanced MOPs, the performance of most MOEA/Ds will evidently deteriorate, as a few solutions will replace most of the others in the evolutionary process, resulting in a significant loss of diversity. To address this issue, this paper suggests a localized decomposition evolutionary algorithm (LDEA) for imbalanced MOPs. A localized decomposition method is proposed to assign a local region for each subproblem, where the inside solutions are associated and the solution update is restricted inside (i.e., solutions are only replaced by offspring within the same local region). Once off-spring are generated within an originally empty region, the best one is reserved for this subproblem to extend diversity. Meanwhile, the subproblem with the largest number of associated solutions will be found and one of its associated solutions with the worst aggregated value will be removed. Moreover, to speed up convergence for each subproblem while balancing the population's diversity, LDEA only evolves the best-associated solution in each subproblem and correspondingly tailors two decomposition methods in the environmental selection. When compared to nine competitive MOEAs, LDEA has shown the advantages in tackling two benchmark sets of imbalanced MOPs, one benchmark set of balanced yet complicated MOPs, and one real-world MOP. • LDEA proposes a localized decomposition algorithm for imbalanced MOPs. • The proposed decomposition method assigns local regions to subproblems. • Mating selection, operators, and environmental selection are modified to cooperate with LDEA. • LDEA outperforms other competitive MOEAs on imbalanced and complicated MOPs. [ABSTRACT FROM AUTHOR]

Abstract: This paper is aimed at the solution of the optimal power flow (OPF) problem with embedded security constraints (OPF-SC) by the particle swarm optimizer. The major objective is to minimize the overall operating cost while satisfying the power flow equations, system security, and equipment operating limits. The overall operating cost is composed by the generation cost, transmission cost, and the consumer benefit. A modification of the conventional particle swarm optimizer (PSO) has been used as the optimization tool, which uses reconstruction operators and dynamic penalization for handling constraints. The reconstruction operators allow the increase of the number of particles within the feasible region. The power equations mismatch, loss active power transmission, and voltages are calculated by the Newton–Raphson method. To demonstrate its robustness, the proposed algorithm was tested on systems from the open literature. Several cases have been studied to test and validate the proposed approach. [Copyright &y& Elsevier]

In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing. [ABSTRACT FROM AUTHOR]

The assessment of strengths and weaknesses of a solver is often limited by the diversity of the cases where it is tested upon. As such, it is paramount to have a versatile tool which finds the problem instances where such a solver excels/fails. In this manuscript, we propose to use an evolutionary algorithm for creating this tool. To validate our approach, we conducted several tests on four heuristics for the knapsack problem. Although, the process can be extended to other domains with relatively few changes. The tests cover different sets of instances, both favoring the performance of one heuristic while hindering that of the remaining ones, and vice versa. To further test our evolutionary-based model, we also apply it on a recent approach that combines the strengths of different heuristics to improve its performance (usually referred to as a hyper-heuristic). We show that it is possible to tailor instances in which even this more complex model excels/fails. Throughout our approach, a researcher can test a solver under different kinds of scenarios, delving deeper into the conditions that make it perform well/poorly. Therefore, we recommend using the proposed approach as a means to grasp better insights about strengths and weaknesses of different solvers. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various article within the issue on topics including the parallel graphics processing unit (GPU) evaluation model, visual tracking using GPU for computation, and EAsy Specification of Evolutionary Algorithm (EASEA) software.

The design of bar structures in civil engineering is a complex problem when dealing with real-world structures. An approach to deal with these problems is to apply metaheuristics, which are stochastic methods based on iteratively producing and evaluating tentative solutions. In particular, we focus on multi-objective metaheuristics, as we consider two goals to be minimized: the weight and the deflection of the structure. When applying these techniques to real-world problems, running a metaheuristic for several thousands of evaluations may require many days on a single processor. In this paper, we develop distributed master/slave versions of four multi-objective metaheuristics that are representative of the state-of-the-art and apply them to optimize the design of two instances of a cable-strayed bridge. Our study reveals that our parallel proposals are able to effectively use up to 450 cores, providing accurate solutions in a short time. [ABSTRACT FROM AUTHOR]

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior. [ABSTRACT FROM AUTHOR]

In civil and industrial engineering, structural design optimization problems are usually characterized by the presence of multiple conflicting objectives, as to get the minimum investment cost and the maximum safety of the final design. This issue makes these problems to have not only one single solution, but a set them. Such solutions represent the possible trade-offs among the different objectives to be optimized. This paper reviews the latest developments in the field of multi-objective metaheuristics for solving design problems focusing on the optimization of the topology, shape, and sizing of civil engineering structures. We review both the algorithms and the applications, and the most relevant features of the solvers and the design optimization problems are analyzed. The paper ends by addressing a number of relevant and open issues that can be the subject of further research. [ABSTRACT FROM AUTHOR]

An introduction to articles published within the issue is presented, including a study on using heuristics in chemotheraphy scheduling, another which describes an artificial bee colony (ABC) based algorithm and one article which proposes an evolutionary algorithm for high-dimensional funtion optimization.

Abstract: Parametric reconfiguration plays a key role in non-iterative concurrent design of mechatronic systems. This is because it allows the designer to select, among different competitive solutions, the most suitable without sacrificing sub-optimal characteristics. This paper presents a method based on an evolutionary algorithm to improve the parametric reconfiguration feature in the optimal design of a continuously variable transmission and a five-bar parallel robot. The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. Differential Evolution is utilized as the search algorithm. The results obtained in a set of five experiments performed per each mechatronic system show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of ε-dominance. Though bounds on the quality of the limit approximation--which are entirely determined by the archiving strategy and the value of ε--have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a ε A, is "large." Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy--multi-objective continuation methods--by showing that the concept of ε-dominance can be integrated into this approach in a suitable way. [ABSTRACT FROM AUTHOR]

In rural power systems, the automatic voltage regulators (AVRs) help to reduce energy losses and to improve the energy quality of electric utilities, compensating the voltage drops through distribution lines. In order to help electric companies in the decision-making process, this paper presents a method to define the optimal location of a set of AVRS in electric distribution networks. The optimization process is treated as a multiobjective problem considering the total power losses and the voltage drops in the system as the objectives to be optimized. A novel technique called micro genetic algorithm (pGA) is used to solve the multiobjective problem. This technique is capable of finding, in a very efficient way, the Pareto optimal solutions, giving the decision maker a set of possible (trade-off) solutions from which to choose. [ABSTRACT FROM AUTHOR]