A key issue in tackling multimodal multi-objective optimization problems (MMOPs) is achieving the balance between objective space diversity and decision space diversity to obtain multiple Pareto sets (PSs) while guara...
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A key issue in tackling multimodal multi-objective optimization problems (MMOPs) is achieving the balance between objective space diversity and decision space diversity to obtain multiple Pareto sets (PSs) while guaranteeing convergence to the Pareto front (PF). However, most of the existing methods for MMOPs face the following two shortages: (i) they put insufficient emphasis on improving decision space diversity, resulting in missing some PSs or PS segments;and (ii) they lack the utilization of promising historical individuals which may help search the PSs. To alleviate these limitations, this paper proposes a novel multi-stage evolutionary algorithm with two improved optimization strategies. Specifically, the proposed method decomposes solving MMOP into two tasks, i.e., the Exploration task and the Exploitation task. The Exploration task first aims to explore the decision space to detect the multiple PSs, then, the Exploitation task aims to enhance the diversities on both objective and decision spaces (i.e., exploiting the PF and PSs). To better search PSs, historical individuals that are well-distributed in the decision space are stored as the evolutionary experience, and then used to generate offspring individuals. Moreover, a new differential evolution is designed to force crowded individuals to move to sparse and undetected regions on the PSs to enhance the diversity of PSs. Extensive experimental studies compare the proposed method with five state-of-the-art methods tailored for MMOPs on two benchmark test suites. The results demonstrate that the proposed method can outperform others in terms of three performance indicators.
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 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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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.
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.
Bilevel evolutionary algorithms (BLEAs) are a plausible approach for bilevel optimization. However, these algorithms require many fitness evaluations (FEs) and might become unusable if the fitness evaluations are comp...
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Bilevel evolutionary algorithms (BLEAs) are a plausible approach for bilevel optimization. However, these algorithms require many fitness evaluations (FEs) and might become unusable if the fitness evaluations are computationally expensive. Therefore, reducing the number of FEs is crucial for designing a BLEA for expensive bilevel optimization (EBLOP). The surrogate-assisted optimization and knowledge transfer mechanisms used in BLEAs have been proven to reduce the number of FEs. This paper proposes a surrogate-assisted bilevel improved multioperator differential evolution algorithm (SA-BL-IMODE), which integrates a classification model-based assisted preselection and environment selection strategy (CPES) for EBLOP. In CPES, promising candidate solutions are prescreened by a classification model, filtering out some unpromising candidate solutions without performing FEs, thus improving the algorithm's performance. Moreover, the classification model also assists environment selection by directly discarding the unpromising offspring solutions before FEs are performed, thus reducing the number of FEs in each iteration. Additionally, an enhanced direct neighbor solution transfer (EDST) mechanism is proposed to identify and utilize the interactions between upper-level and lower-level variables for acquiring knowledge, improving knowledge quality, and reducing the number of FEs further. Experimental studies on two test suite benchmark problems are conducted, and the proposed method is compared with nine state-of-the-art algorithms. The experimental results demonstrate the effectiveness of the proposed mechanisms and show that SA-BL-IMODE has a significant advantage over existing algorithms for expensive bilevel optimization.
This paper presents neuro-augmented vision for evolutionary robotics (NAVER), which aims to address the two biggest challenges in camera-equipped robot evolutionary controllers. The first challenge is that camera imag...
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This paper presents neuro-augmented vision for evolutionary robotics (NAVER), which aims to address the two biggest challenges in camera-equipped robot evolutionary controllers. The first challenge is that camera images typically require many inputs from the controller, which greatly increases the complexity of optimising the search space. The second challenge is that evolutionary controllers often cannot bridge the reality gap between simulation and the real world. This method utilises a variational autoencoder to compress the camera image into smaller input vectors that are easier to manage, while still retaining the relevant information of the original image. Automatic encoders are also used to remove unnecessary details from real-world images, in order to better align with images generated by simple visual simulators. NAVER is used to evolve the controller of a robot, which only uses camera inputs to navigate the maze based on visual cues and avoid collisions. The experimental results indicate that the controller evolved in simulation and transferred to the physical robot, where it successfully performed the same navigation task. The controller can navigate the maze using only visual information. The controller responds to visual cues and changes its behaviour accordingly. NAVER has shown great potential as it has successfully completed (so far) the most complex vision-based task controller in evolutionary robotics literature.
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