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In this paper, we focus on necessary and sufficient efficiency conditions for optimization problems with multiple objectives and a feasible set defined by interval-valued functions. A new concept of Fritz-John and Karush–Kuhn–Tucker-type points is introduced for this mathematical programming problem based on the gH-derivative concept. The innovation and importance of these concepts are presented from a practical and computational point of view. The problem is approached directly, without transforming it into a real-valued programming problem, thereby attaining theoretical results that are more powerful and computationally more efficient under weaker hypotheses. We also provide necessary conditions for efficiency, which have been inexistent in the relevant literature to date. The identification of necessary conditions is important for the development of future computational optimization techniques in an interval-valued environment. We introduce new generalized convexity notions for gH-differentiable interval-valued problems which are a generalization of previous concepts and we prove a sufficient efficiency condition based on these concepts. Finally, the efficiency conditions for deterministic programming problems are shown to be particular instances of the results proved in this paper. The theoretical developments are illustrated and justified through several numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, an LMI-based anti-windup approach is proposed for a class of uncertain multiple input multiple output (MIMO) systems subject to actuator saturation with linear active disturbance rejection controller (LADRC). A modified linear extended state observer (MLESO) is presented to deal with the actuator saturation and the observation error is proved to be bounded. Meanwhile, local asymptotic stability of the closed-loop system is achieved and in the meantime estimate of the basin of attraction is obtained through formulating anti-windup gain matrix K c . Furthermore, estimate of basin of attraction of the closed-loop system is maximized through solving a convex optimization problem. Finally, numerical examples are provided to illustrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two evolutionary algorithms, Cultural Algorithm (CA) and Differential Evolution (DE) algorithm. This hybridization follows the HTH (High-level Teamwork Hybrid) nomenclature in which two meta-heuristics are executed in parallel. The new algorithm named as CADE, manages an overall population which is shared between CA and DE simultaneously. Four modified knowledge sources have been used in proposed CA which are: topographical, situational, normative and domain. The role of the used acceptance function in belief space is to select the knowledge of the best individuals to update the current knowledge. A novel quality function is used to determine the participation ratio for both CA and DE, and then a competitive selection takes place in order to select the proportion of function evaluations allocated for each technique. This collaborative synergy emerges between the DE and CA techniques and is shown to improve the quality of solutions, beyond what each of these two algorithms alone. The performance of the algorithm is evaluated on a set of 50 scalable optimization problems taken from two sources. The first set of 35 came from existing benchmark sets available in the literature. The second set came from the 2014 IEEE Single Function optimization competition. The overall results show that CADE has a favorable performance and scalability behaviors when compared to other recent state-of-the-art algorithms. CADE's overall performance ranked at number 1 for each of the two sets of problems. It is suggested that CADE's success across such a broad spectrum of problem types and complexities bodes well for its application to new and novel applications. [ABSTRACT FROM AUTHOR]

Denoeux proposed an operator called the cautious conjunctive rule (CCR) to combine non-dogmatic belief functions (i.e., the frame is considered a focal element) from reliable dependent sources, which can occur in practice. However, in cases such as uncertain multiple attribute and group decision analyses, belief functions may denote data that vary in importance. This paper extends CCR as a weighted CCR (WCCR) to combine belief functions in consideration of their relative weights in such situations. Properties of WCCR are analyzed, proven, and demonstrated using numerical examples. In particular, the consideration of relative weights enables WCCR to combine dogmatic belief functions. The normalized WCCR is further constructed and used to assess the trustworthiness of a hospital information system employed in many hospitals in Anhui Province, China. [ABSTRACT FROM AUTHOR]

This paper presents a novel meta-heuristic optimization algorithm, named Adaptive Simplified Human Learning Optimization (ASHLO), which is inspired by the human learning mechanisms. Three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to generate new solutions and search for the optima by mimicking the learning behaviors of humans. The numerical functions, deceptive functions and 0–1 knapsack problems are adopted as benchmark problems to validate the performance of ASHLO, and the results are compared with those of binary particle swarm optimization (BPSO), modified binary differential evolution (MBDE), the binary fruit fly optimization algorithm (bFOA) and adaptive binary harmony search (ABHS). The experimental results demonstrate that the developed ASHLO significantly outperforms BPSO, MBDE, bFOA and ABHS and has a robust search ability for various problems. With the adaptive strategy, the search ability of ASHLO is improved further especially on the high-dimensional and complicated problems. Considering the ease of implementation, the excellence of global search ability and the robustness for various problems, ASHLO is a promising optimization tool for scientific research and engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a new fuzzy reasoning method by optimizing the similarity of truth-tables (OS method for short). Its basic idea is to find a fuzzy set such that the truth-tables generated by the antecedent rule and the consequent rule are as similar as possible. Based on this idea, the principle of OS method and the fuzzy reasoning with OS method are given and discussed. And then the OS methods with certain similarity measure and several fuzzy implications are investigated. Finally, numerical examples are analyzed to compare the proposed method with compositional rule of inference (CRI) method. [ABSTRACT FROM AUTHOR]

Abstract: Nature-inspired algorithms for optimization are significant topics in the areas of computational intelligence. The contribution of this paper is to present a new heuristic intelligent evolutionary algorithm based on membrane systems to solve the global numerical optimization problems. The proposed algorithm employs the fundamental ingredients of membrane systems, including multisets, reaction rules and membrane structure. In addition, the proposed algorithm incorporates information of the adjacent symbol-objects, to guide the evolution toward the global optimum, efficiently. More specifically, symbol-objects are evolved by the cellular automata model which invokes the rewrite rules to exchange the information of the adjacent symbol-objects. Moreover, sharing information in the skin membrane is implemented, which accelerates the speed of the proposed algorithm to find the global optimal solution. In the extensive experimental study, the effectiveness of the proposed algorithm is demonstrated with the benchmark global numeric optimization problems. The experimental results indicate that the proposed method is a competitive optimizer in comparison with the four state-of-the-art evolutionary algorithms. [Copyright &y& Elsevier]