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Abstract: Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique for global optimization over continuous spaces. Few variants of TLBO have been proposed by researchers to improve the performance of the basic TLBO algorithm. In this paper the authors investigate the performance of a new variant of TLBO called modified TLBO (mTLBO) for global function optimization problems. The performance of mTLBO is compared with the state-of-the art forms of Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms. Several advanced variants of PSO, DE and ABC are considered for the comparison purpose. The suite of benchmark functions are chosen from the competition and special session on real parameter optimization under IEEE Congress on Evolutionary Computation (CEC) 2005. Statistical hypothesis testing is undertaken to demonstrate the significance of mTLBO over other investigated algorithms. Finally, the paper investigates the data clustering performance of mTLBO over other evolutionary algorithms on a few standard synthetic and artificial datasets. Results of our work reveal that mTLBO performs better than many other algorithms investigated in this work. [Copyright &y& Elsevier]

Abstract This paper proposes a multimodal multiobjective Differential Evolution optimization algorithm (MMODE). The technique is conceived for deployment on problems with a Pareto multimodality, where the Pareto set comprises multiple disjoint subsets, all of which map to the same Pareto front. A new contribution is the formulation of a decision-variable preselection scheme that promotes diversity of solutions in both the decision and objective space. A new mutation-bound process is also introduced as a supplement to a classical mutation scheme in Differential Evolution methods, where offspring that lie outside the search bounds are given a second opportunity to mutate, hence reducing the density of individuals on the boundaries of the search space. New multimodal multiobjective test functions are designed, along with analytical expressions for their Pareto sets and fronts. Some test functions introduce more complicated Pareto-front shapes and allow for decision-space dimensions greater than two. The performance of the MMODE algorithm is compared with five other state-of-the-art methods. The results show that MMODE realizes superior performance by finding more and better distributed Pareto solutions. Highlights • A multimodal multi-objective optimization with differential evolution method is proposed to solve multimodal multi-objective optimization problems. • A preselection scheme and a mutation bound processing method are designed to obtain multiple subsets in the Pareto set and improve their distribution. • Novel test functions with different Pareto set shapes are designed to verify the validity of the proposed algorithm. • Compared with some existing multi-objective optimization algorithms, the proposed algorithm shows statistical superiority. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel framework for improving the performance of multi-population algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the proposed framework is to incorporate the concept of scheduling into multi-population methods with the aim to allocate more function evaluations to the best performing sub-populations. Two methods are developed based on the proposed framework, each of which uses a different approach for scheduling the sub-populations. The first method combines the quality of sub-populations and the degree of diversity among them into a single feedback parameter for detecting the best performing sub-population. The second method uses the learning automata as the central unit for performing the scheduling operation. In order to validate the applicability of the proposed methods, they are incorporated into three well-known algorithms for DOPs. The experimental results show the efficiency of the scheduling approach for improving the multi-population methods on the moving peaks benchmark (MPB) and generalized dynamic benchmark generator. [ABSTRACT FROM AUTHOR]

Abstract: In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented. [Copyright &y& Elsevier]

This paper gives a review of recent extensions of the Differential Evolution (DE) algorithm for use in Large-Scale Global Optimization (LSGO) and presents an empirical analysis of DE-based and some other state-of-the-art algorithms for LSGO on the CEC 2013 LSGO benchmark suite. Since witnessing the first successful applications of DE for a wide variety of optimization problems in the early nineties, researchers have developed several new algorithms in this field. In this paper, we are especially interested in algorithms for solving LSGO. As LSGO is one of the most active research lines, not only in DE, but in many evolutionary and meta-heuristic algorithms, we discuss general approaches for dealing with LSGO first. The main focus of the paper is DE. We review its basic algorithm and discuss several extensions used for coping with large-scale problems. This paper has two main objectives: (1) To propose, from a theoretical point of view, the grouping of DE mechanisms for dealing with LSGO into nine groups, and (2) To evaluate sixteen recently proposed algorithms for LSGO empirically. Many benchmark suites were designed with the aim of providing a suitable evaluation platform for testing and comparing large-scale optimization algorithms. In this paper, the CEC 2013 LSGO benchmark suite was chosen for comparison, because it resembles the following features of real-world problems: Non-uniform subcomponent sizes; imbalance in the contribution of subcomponents; and functions with interdependent overlapping subcomponents. The performances of state-of-the-art algorithms are compared, and the algorithms are ranked using three different metrics, which evaluate the performance from different perspectives. The conducted research shows that DE is among the best algorithms for LSGO on the CEC 2013 LSGO benchmark suite, especially when used with other mechanisms for dealing with large numbers of variables. Finally, the analysis has shown that there is still some room for further improvements in DE towards the solution of LSGO problems. • A review of Large Scale Global Optimization (LSGO). • Focus on recent use of Differential Evolution (DE). • Grouping of mechanisms, used in DE, to cope with LSGO. • Evaluation of 16 state-of-the-art algorithms on the CEC 2013 LSGO benchmark suite. • Empirical analysis performed using three different evaluation methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an optimization method based on differential evolution algorithm and its novel application to extract easy to understand summary for improving text readability. The idea is to improve the readability of the given text for reading difficulties using assistive summary. In order to extract easy to understand summary from the given text, an improved differential evolution algorithm is proposed. A new chromosome representation that considers ordering and similarity for extracting cohesive summary. Also a modified crossover operator and mutation operator are designed to generate potential offspring. The application of differential evolution algorithm for maximizing the average similarity and informative score in the candidate summary sentences is proposed. We applied the proposed algorithm in a corpus of educational text from ESL text books and in graded text. The results show that the summary generated using Differential Evolution algorithm performs better in accuracy, readability and lexical cohesion than existing techniques. The task based evaluation done by target audience also favors the significant effect of assistive summary in improving readability. [Copyright &y& Elsevier]

This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch's geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms. [ABSTRACT FROM AUTHOR]

Abstract: For effective multi-document summarization, it is important to reduce redundant information in the summaries and extract sentences, which are common to given documents. This paper presents a document summarization model which extracts key sentences from given documents while reducing redundant information in the summaries. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The model is represented as a discrete optimization problem. To solve the discrete optimization problem in this study an adaptive DE algorithm is created. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is to be preferred over summarization systems. We also showed that the resulting summarization system based on the proposed optimization approach is competitive on the DUC2002 and DUC2004 datasets. [Copyright &y& Elsevier]

Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions. [ABSTRACT FROM AUTHOR]