An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Kong, Fang; Jiang, Jianhui; Huang, Yan

doi:10.3390/math7060521

Cited by 15 publications

(9 citation statements)

References 36 publications

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“…Therefore, individuals in a population, or combinations of steps that could lead to the solution of the complex problem, in the end, can diminish from the current population, and become extinct in an evolutionary context, and thus fail to find high-quality solutions. Motivated by such results, the novelty method is proposed [22] in which the search process is driven primarily by phenotypic diversity. It is shown that scaling with complex search domains is better than those of traditional objective-based fitness functions.…”

Section: Methodsmentioning

confidence: 99%

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Diversity Maintenance for Efficient Robot Path Planning

Ćurković¹,

Čehulić²

2020

Applied Sciences

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Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.Path planning can generally be classified based on the nature of the environment as static or dynamic. In the case of the static environment, the layout, once perceived by the robot remains unchanged. Dynamic environments include movable objects i.e., other robots.According to the algorithm used for planning, the planner can either be local-meaning sensor-based and reactive, or global-meaning map-based and planning the trajectory from the initial to the final position at once. Based on the completeness, planning can be classified either as complete or heuristic. In the case of complete planning, the algorithm will always find a path in finite time if the path exists, or report that no feasible path exists in finite time. These algorithms are preferred for the systems with a low number of degrees of freedom over the heuristics algorithms [2][3][4][5][6].In the case of a 3D problem considered in this paper, both the trajectory and the robot's body have to avoid obstacles present in the environment and thus checked for collisions. This makes planning significantly more complex when compared to simple point-wise planning. It is known that for similar problems, population-based heuristic planning methods, i.e., swarm and evolutionary approaches are successfully utilized to find high-quality solutions in acceptable time [7][8][9][10]. It is also proven that the path planning problem is NP-complete, with the complexity of tasks increasing exponentially with the DOF number [11]. This is an additional motivation of exploring alternative methods to path planning, seeking for high quality (instead of optimal) solut...

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Section: Methodsmentioning

confidence: 99%

“…Motivated by such results, the novelty method is proposed [22] in which the search process is driven primarily by phenotypic diversity. It is shown that scaling with complex search domains is better than those of traditional objective-based fitness functions.…”

Section: Introductionmentioning

confidence: 99%

Diversity Maintenance for Efficient Robot Path Planning

Ćurković¹,

Čehulić²

2020

Applied Sciences

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“…Competitive strategy was used in [30] to manage convergence, while entropy measurement was employed to maintain the diversity of the swarm. In [31], an adaptive multi-swarm competition PSO was proposed where the swarm is adaptively divided into sub-swarms and a competition mechanism is used to maintain diversity in the swarms. The sub-swarms slowly converge, adaptively reducing the number of swarms while balancing between exploration and exploitation tendencies.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

et al. 2020

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Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.

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“…Hence, solutions move and interact, rather than being evolved as in EAs, according to the dynamics outlined in Reference [25]. Several PSO variants have been designed to deal with a wide range of problems [26], including large scale ones [27,28], as well as challenging engineering applications [29], and hybrid versions were also designed thus generating effective PSO based multi-strategy approaches [30] and Estimation of Distribution Algorithms (EDAs) [31,32]. The EDA framework is quite interesting and has proven to be successful over different fields such as Robotics [33] and combinatorial domains [21].…”

Section: Background and Related Workmentioning

confidence: 99%

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

2019

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This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.

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An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Cited by 15 publications

References 36 publications

Diversity Maintenance for Efficient Robot Path Planning

Diversity Maintenance for Efficient Robot Path Planning

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

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