Customer segmentation is a challenging task in marketing that aims to build homogeneous segments of customers based on their similar characteristics and activities. This problem is considered multi-objective since it ...
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Customer segmentation is a challenging task in marketing that aims to build homogeneous segments of customers based on their similar characteristics and activities. This problem is considered multi-objective since it requires the evaluation of several variables including descriptive and predictive characteristics of customers. However, given that most exiting segmentation methods are based on the optimisation of a single-objective function, the identification of homogeneous customer segments in terms of both predictive and descriptive variables becomes a major issue. Descriptive and predictive characteristics are usually considered as two different and independent objectives, which cannot be optimised together. To deal with this problem, we propose a multi-objective segmentation approach based on three conceptual axes: descriptive, predictive, and quality-validation. In addition to the specificity of design of the multi-objective model, our proposed approach has the specificity of directly optimising the multi-objective problem using a customised genetic algorithm that directly approximates a set of Pareto-optimal solutions. We have applied and evaluated the proposed approach in an empirical study which aims to segment bank credit card customers using their descriptive characteristics and their predictive behaviour. Obtained results have shown the ability of the proposed approach to look for effective homogeneous segments and help decision-makers propose more tailored marketing strategies.
In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding bu...
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In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding but introducing restrictions for each collision found. Afterward, a co-evolutionary optimization process is implemented for introducing changes in the initial paths to find a combination of routes that is collision-free. The approach has been tested in mazes with increasing the number of robots, showing a robust performance although at high time expenses. Nevertheless, several enhancements are proposed to tackle this issue.
It is desired to make the replication portfolio when a benchmark portfolio has delivered good returns. However, the portfolio replication problem is one of equality constrained indeterminate problems. We cannot find t...
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The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with chang...
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The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with changing data and parameters. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, to deal with such problems, a new evolutionary framework with multiple novel mechanisms is proposed. The new mechanisms are for (1) dealing with both linear and non-linear components in the constraint functions, (2) identifying the rate of change in the coefficients of the variables and (3) updating the population efficiently after every change occurs in the problem. To evaluate the per-formance of the proposed algorithm, we designed a new set of 13 dynamic benchmark problems, each of which consists of 20 dynamic changes and 3 different scenarios. The results demonstrate that the proposed algorithm significantly contributes in achieving good quality solutions, high fea-sibility rates and fast convergence in rapidly changing environments. In addition, the framework shows its capability of using different meta-heuristics to solve dynamic problems.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
This paper presents a personal account of the author's 35 years "adventure" with evolutionary Computation-from the first encounter in 1988 and many years of academic research through to working full-time...
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This paper presents a personal account of the author's 35 years "adventure" with evolutionary Computation-from the first encounter in 1988 and many years of academic research through to working full-time in business-successfully implementing evolutionary algorithms for some of the world's largest corporations. The paper concludes with some observations and insights.
This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Sin...
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This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.
This study proposes an optimization method based on qualitative scores, specifically focusing on the similarity to nature-like features. The key to the proposed method is the utilization of a deep neural network train...
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Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMO...
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Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.
In most of distributed evolutionary algorithms (DEAs), migration interval is used to decide the frequent of migration. Nevertheless, a predetermined interval cannot match the dynamic situation of evolution. Consequent...
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Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary al...
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Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions' fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models.
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