In the recent literature a popular algorithm namely 'Competitive Swarm Optimizer (CSO)' has been proposed for solving unconstrained optimization problems that updates only half of the population in each iterat...
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In the recent literature a popular algorithm namely 'Competitive Swarm Optimizer (CSO)' has been proposed for solving unconstrained optimization problems that updates only half of the population in each iteration. A modified CSO (MCSO) is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO. A small change in CSO makes a huge difference in the solution quality. The basic idea behind the proposition is to maintain a higher rate of exploration to the search space with a faster rate of convergence. The proposed MCSO is applied to solve the standard CEC2008 and CEC2013 large scale unconstrained benchmark optimization problems. The empirical results and statistical analysis confirm the better overall performance of MCSO over many other state-of-the-art meta-heuristics, including CSO. In order to confirm the superiority further, a real life problem namely 'sampling-based image matting problem' is solved. Considering the winners of CEC 2008 and 2013, MCSO attains the second best position in the competition. (C) 2017 Elsevier B.V. All rights reserved.
Wilting point is an important parameter indicating the inhibition of plant transpiration processes, which is essential for green infrastructures. Generalization of wilting point is very essential for analyzing the hyd...
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Wilting point is an important parameter indicating the inhibition of plant transpiration processes, which is essential for green infrastructures. Generalization of wilting point is very essential for analyzing the hydrological performance of green infrastructures (e.g. green roofs, biofiltration systems) and ecological infrastructures (wetlands). Wilting point of various species is known to be affected by the factors such as soil clay content, soil organic matter, slope of soil water characteristic curve at inflection point (i.e., s index) and fractal dimension. Therefore, its practical usefulness forms the strong basis in developing the model that quantify wilting point with respects to the deterministic factors. This study proposes the wilting point model development task based on optimization approach of Genetic programming (GP) with respect to the input variables (soil clay content, soil organic matter, s-index and fractal dimension) for various type of soils. The GP model developed is further investigated by sensitivity and parametric analysis to discover the relationships between wilting point and input variables and the dominant inputs. Based on newly developed model, it was found that wilting point increases with fractal dimension while behaves highly non-linear with respect to clay and organic content. The combined effect of the clay and organic content was found to greatly influence the wilting point. It implies that wilting point should not be generalized as usually done in literature. (C) 2017 Elsevier Ltd. All rights reserved.
Heat treatment is an essential process in many production systems, which is generally carried out in a heat exchanger network (HEN). The major complication arisen in heat treatment is the fouling due to the deposition...
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Heat treatment is an essential process in many production systems, which is generally carried out in a heat exchanger network (HEN). The major complication arisen in heat treatment is the fouling due to the deposition of unwanted particles on heat exchanger surfaces. The difficulties, faced in mitigating the fouling by improving the design of heat exchangers or controlling process parameters, necessitate periodic cleaning of the heat exchangers for reinstating their performances. Accordingly, a HEN is desired to schedule in a way to minimize the cleaning cost satisfying various process conditions. In such an attempt, three mixed-binary evolutionary algorithms (EAs) are investigated here for scheduling a HEN engaged in milk pasteurization, in which the growth rate of fouling is comparatively very high. The experimental results depict that the minimum cleaning cost, however, is accompanied with overheating of milk consuming excess energy and a higher outlet temperature of the heating medium (steam) causing excess requirement of steam. Therefore, the scheduling of the HEN is also handled as a multi-objective optimization problem for simultaneously minimizing the cleaning cost, overheating of milk and flow rate of steam, in which the EAs could maintain a better balance among the three conflicting objectives. (C) 2017 Elsevier B.V. All rights reserved.
Miniature autonomous sensory agents (MASA) can play a profound role in the exploration of hardly accessible unknown environments, thus, impacting many applications such as monitoring of underground infrastructure or e...
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An Iterative Differential evolutionary Algorithm is proposed for the optimized identification of sets of parameter values for a system whose analysis is very time demanding and for which it is difficult to identify an...
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The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capabi...
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The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive epsilon-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.
Sequential learnable evolutionary algorithm (SLEA) provides an algorithm selection framework for solving the black box continuous design optimization problems. An algorithm pool consists of set of established algorith...
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In this paper, the evolutionary algorithm is applied to obtain the adaptive wavelet feature parameters to represent High Resolution Range Profile (HRRP) andtrain the adaptive wavelet neural network to classify HRRP. T...
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To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. ...
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To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature. When pairs of agents interact for a topic, their actions are introduced to opinion updates with bounded confidence. Simulation results show that a transition from a disordered state to a consensus state occurs at a critical point of the tolerance threshold, which depends on the opinion dimension. The critical point increases as the dimension of opinions increases. Multiple topics promote opinion interactions and lead to the formation of macroscopic opinion clusters. In addition, more topics accelerate the evolutionary process and weaken the effect of network topology. We use two sets of large-scale real data to evaluate the model, and the results prove its effectiveness in characterizing a real evolutionary process. Our model achieves high performance in individual action prediction and even outperforms state-of-the-art methods. Meanwhile, our model has much smaller computational complexity. This paper provides a demonstration for possible practical applications of theoretical opinion dynamics. Published by AIP Publishing.
Lennard-Jones clusters are the best-known benchmark for global cluster structure optimization. For a few cluster sizes, the landscape is deceptive, featuring several funnels, with the global minimum not being in the w...
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Lennard-Jones clusters are the best-known benchmark for global cluster structure optimization. For a few cluster sizes, the landscape is deceptive, featuring several funnels, with the global minimum not being in the widest one. More than a decade ago, several non-deterministic global search algorithms were presented that could solve these cases, mostly using additional tools to ensure structural diversity. Recently, however, many publications have advertised new search algorithms, claiming efficiency but being unable to solve these harder benchmark cases. Here, we demonstrate that evolutionary algorithms can solve these hard cases efficiently, if enhanced with one of several very different diversity measures (niching) which were set up in an ad-hoc way, without extensive deliberation, testing or tuning. Hence, these hard benchmark cases should definitely be considered solvable. Additionally, these niching concepts offer insights into the different Lennard-Jones structural types, and into the way niching works in evolutionary algorithms. (C) 2016 Elsevier B.V. All rights reserved.
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