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The case study of plant intelligence inspired novel non-swarm intelligence (NSI) algorithms, namely Venus Flytrap Optimization and Bladder-Worts Suction, concentrated in this paper. These algorithms devised on the prey-hunting mechanisms of the Venus Flytrap (Dionaea Muscipula) and BladderWorts (Utricularia) plants, respectively. A comparative view of these algorithms is discussed. The main-support criterion is the major characteristic of these approaches. The benefits of this main-support criterion and their performances are evidenced with a case study of extracting the highly correlated maximal local patterns in gene expression data through biclustering. The NSI algorithms are proposed for biclustering gene expression data in this paper. The results are compared with existing optimization techniques like PSO and GA, and biclustering approaches like Cheng and Church, OPSM, BiMax, and Plaid approaches. This analysis evidenced the performance of NSI algorithms can yield optimal maximal local patterns with high correlation. Further, various real-time research applications of NSI approaches are also discussed. [ABSTRACT FROM AUTHOR]

The rapid growing number of web services has posed new challenges for service composition computing. How to combine services that meet the needs of users in the least amount of time from a huge number of candidate services is a hot topic of research today. As a meta-heuristic algorithm for solving optimization problems, salp swarm algorithm (SSA) has been widely applied to case scenarios in different fields due to its simple structure and high performance. However, QoS-aware service composition is a discrete problem and existing methods are not suitable for it. Therefore, in this paper, we propose an improved SSA integrating chaotic mapping method for QoS service composition selection, named CSSA. Through the randomness and ergodicity of chaos, reducing the possibility of falling into local optimum and strengthening the exploitation capability of the algorithm. In addition, a fuzzy continuous neighborhood search method is used to enhance the local search capability of the algorithm which makes the discrete space of service composition in a way similar to continuous space. Finally, two well-known datasets are used to verify the effectiveness of CSSA compared to three advanced algorithms and original SSA. The test results demonstrate that CSSA has significant advantages and it also has satisfactory performance in large scale scenarios. [ABSTRACT FROM AUTHOR]

In real life, some complex problems are multi-objective optimization problems. Most of the existing studies have focused on how to obtain the optimal solutions distributed on the whole Pareto-optimal frontier. However, in some fields such as industrial production, the decision-makers of enterprises usually care about what specific measures can maximize the comprehensive benefits of enterprises. Due to this kind of realistic demands, we prefer to find a small part of the optimal solutions according to the preference information suggested by the decision-makers rather than obtain all of the Pareto-optimal solutions. However, almost all of the existing methods only repeat calculation when they meet the scenario where the user-preferences change over time. To address the multi-objective optimization problem under the scenario with dynamic user-preferences information, we propose a MOEA/D-DPRE (multi-objective optimization algorithm based on dynamic preference information) algorithm in this paper, and its framework is inspired by the MOEA/D-PRE (decomposition user-preference multi-objective evolutionary) algorithm. We analyze the four position relations between the distribution region of the old preference weight vectors (old preference region), and we also present the distribution region of the new preference weight vectors (new preference region) and propose the different strategy to the different case respectively. When the preference information changes, the MOEA/D-DPRE can converge to the region of new interest by responding to the change of preference and the historical information. Experimental results show that the proposed method is better than the compared method in convergence speed and distribution under the scenario with dynamic user-preferences information. [ABSTRACT FROM AUTHOR]

Evolutionary computation has been little, but steadily, used in the CAD community during the past 20 years. Nowadays, due to their overwhelming complexity, significant steps in the validation of microprocessors must be performed on silicon, i.e., running experiments on physical devices after tape-out. The scenario created new space for innovative heuristics. This paper shows a methodology based on an evolutionary algorithm that can be used to devise assembly programs suitable for a range of on-silicon activities. The paper describes how to take into account complex hardware characteristics and architectural details. The experimental evaluation performed on two high-end Intel microprocessors demonstrates the potentiality of this line of research. [ABSTRACT FROM AUTHOR]

In this paper, denoising of smooth ( H10-regular) images is considered. The purpose of the paper is basically twofold. First, to compare the denoising methods based on L1- and L2-fitting. Second, to analyze and realize an active-set method for solving the non-smooth optimization problem arising from the former approach. More precisely, we formulate the algorithm, proof its convergence, and give an efficient numerical realization. Several numerical experiments are presented, where the convergence of the proposed active-set algorithm is studied and the denoising properties of the methods based on L1- and L2-fitting are compared. Also a heuristic method for determining the regularization parameter is presented and tested. [ABSTRACT FROM AUTHOR]

Researchers often comment on the popularity and potential of nature-inspired meta-heuristics (NIM), however there has been a paucity of data to directly support the claim that NIM are growing in prominence compared to other optimization techniques. In a companion article published in this special issue, I reported evidence that the use of NIM is not only growing, but indeed has surpassed mathematical optimization techniques (MOT) and other metaheuristics in several metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article reviews several theories of algorithm utility and discusses why these arguments remain unsatisfying. I argue that any explanation of NIM popularity should directly account for the manner in which most NIM success has actually been achieved: through hybridization and customization to specific problems. By taking a problem lifecycle perspective, this paper provides simple yet important insights into how nature-inspired meta-heuristics might derive utility by being flexible. Given global trends in the evolution of business products and services where optimization algorithms are applied, I speculate that highly flexible algorithm frameworks will become increasingly popular within our rapidly changing world. [ABSTRACT FROM AUTHOR]

Although researchers often comment on the popularity of nature-inspired meta-heuristics (NIM), there has been very little empirical data to support the claim that NIM are growing in prominence compared to other optimization techniques. This paper presents strong evidence that the use of NIM is not only growing, but indeed appears to have surpassed mathematical optimization techniques and other meta-heuristics in several metrics related to academic research activity (publication frequency) and commercial activity (patenting frequency). Motivated by these findings, this article discusses some of the possible origins of this growing popularity including historical bias, conceptual appeal, simplicity of implementation, and algorithm utility. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach to formal algorithm design for a typical class of discrete optimization problems. Using a concise set of program calculation rules, our approach reduces a problem into subproblems with less complexity based on function decompositions, constructs the problem reduction graph that describes the recurrence relations between the problem and subproblems, from which a provably correct algorithm can be mechanically derived. Our approach covers a large variety of algorithms and bridges the relationship between conventional methods for designing efficient algorithms (including dynamic programming and greedy) and some effective methods for coping with intractability (including approximation and parameterization). [ABSTRACT FROM AUTHOR]

In the paper we deal with lower bounds constructed for the asymptotic competitive ratio of semi-online bin packing and batched bin packing algorithms.We determine the bounds as the solutions of a related nonlinear optimization problem using theoretical analysis and a reliable numerical global optimization method. Our results improve the lower bounds given in Gutin et al. (Discrete Optim 2:71–82, 2005) for some special cases of the batched bin packing problem. [ABSTRACT FROM AUTHOR]