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Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon.

To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms.

The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark.

This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

The purpose of this paper is to discuss the application of computational intelligence techniques to the field of industrial robot control.

The core ideas behind using neural computation, evolutionary computation, and fuzzy logic techniques are presented, along with a selection of specific real‐world applications.

Their practical advantages and disadvantages relative to more traditional approaches are made clear.

The reader will appreciate the power of computational intelligence techniques for industrial robot control, and hopefully be encouraged to explore further the possibility of using them to achieve improved performance in their own applications.

User-centered product designs have been attracting increasing attention, particularly in digital design. In interacting with the design support system, designers may face problems such as changing demands (e.g. unclear demands) and insufficient descriptions of these demands (e.g. data scarcity). The purpose of this paper is to build a design support system prototype for demonstrating the feasibility of meeting the high involvement of users in digital products.

Interactive evolutionary computation is applied.

A prototype of self-design greeting card system (SDGCS) was proposed. It provides professional design layouts, offers users numerous self-design models, and allows nonprofessional users to easily design greeting cards. The results of this study show that users were satisfied with the functionality, usefulness, and ease-of-use of the SDGCS.

This study used digital card design as an example for demonstrating the feasibility of satisfying the unclear needs of uses, enabling users to design a digital card creatively and complete their designs quickly. However, the current system only supports the design of static objects and layout of card. And the evaluation sample size was small, which might affect generalizability of the findings.

In practice, greeting card web operators can image the feasible business models by providing the attraction of self-design functionalities.

In current human-centric marketing era, consumers have begun to request interaction with designers in creating the value of a product. However, very few previous studies have provided support for digital product self-design. This study demonstrated the feasibility of satisfying the needs of self-design.

The purpose of this paper is to show how intelligence techniques have been used in information management systems.

The results of a literature review on intelligence decision systems used in enterprise information management are analyzed. The intelligence techniques used in enterprise information management are briefly summarized.

Intelligence techniques are rapidly emerging as new tools in information management systems. Especially, intelligence techniques can be used to utilize the decision process of enterprises information management. These techniques can increase sensitiveness, flexibility and accuracy of information management systems. The hybrid systems that contain two or more intelligence techniques will be more used in the future.

The intelligence decision systems are briefly introduced and then a literature review is given to show how intelligence techniques have been used in information management systems.

Lists eight criteria for designing a general trading system for investment, explains how the five steps of genetic (computer) programming work in practice and shows how they can be applied to identify trading rules for a particular stock and stock screening rules for portfolio formation. Warns of some potential problems but believes the system described meets the eight criteria set and is easy to implement.

The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration.

The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead.

The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm.

The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.

Particle swarm optimization (PSO) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, a great deal of work remains to be done to improve the particle swarm performance. The purpose of this paper is to present a new adaptive PSO approach to overcome convergence drawbacks. Thus, the updating of the particle position rule and the introduction of new acceleration parameter augment the performance of the proposed model developed in this perspective.

In the studied picture, each particle defined in a multidimensional search space is represented by a vector of three adaptive parameters representing, respectively, the adaptive cognitive factor, the adaptive social factor, and the bi‐acceleration factor. Therefore, to updating its position rule, the authors add a gaussian noise to each updated velocity in order to increase the diversity in the population swarm.

The simulation experiments uses the CEC, 2005 functions benchmark. The achieved results show that the proposed model improves the existing performance of other algorithms compared to the same benchmark.

The proposed algorithm improves the performance of the PSO based on the self‐adaptation strategy. Thus, it can actually resolve hard functions which introduces noisy and shifted functions.

The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.

To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.

Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand.

The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.

A variety of phenomena such as world wide web, social or business networks, interactions are modelled by various kinds of networks (such as the scale free or preferential attachment networks). However, due to the model‐specific requirements one may want to rewire the network to optimize the communication among the various nodes while not overloading the number of channels (i.e. preserving the number of edges). The purpose of this paper is to present a formal framework for this problem and to examine a family of local search strategies to cope with it.

This is mostly theoretical work. The authors use rigorous mathematical framework to set‐up the model and then we prove some interesting theorems about it which pertain to various local search algorithms that work by rerouting the network.

This paper proves that in cases when every pair of nodes is sampled with non‐zero probability then the algorithm is ergodic in the sense that it samples every possible network on the specified set of nodes and having a specified number of edges with nonzero probability. Incidentally, the ergodicity result led to the construction of a class of algorithms for sampling graphs with a specified number of edges over a specified set of nodes uniformly at random and opened some other challenging and important questions for future considerations.

The measure‐theoretic framework presented in the current paper is original and rather general. It allows one to obtain new points of view on the problem.

In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems.

Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept.

Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm.

The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.