Optimization of Multimodal Benchmark Functions Using Fish Cooperative Hunting Behaviors

Rahman, Imran; Mohamad–Saleh, Junita; Sulaiman, Noorazliza

doi:10.1109/ecace.2019.8679458

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…To demonstrate the validity of the proposed method, it is evaluated on commonly used, six 30-dimensional ( D ) , nonlinear benchmark functions: Sphere function, Rosenbrock function, Sum Squares function, Rastrigin Function, Griewank function, and Ackley function (Izci et al, 2020; Jamil and Yang, 2013; Rahman et al, 2019). Within them, the first three are unimodal functions, and the last three are multi-modal functions.…”

Section: Dppsomentioning

confidence: 99%

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Xin

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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The economic dispatch (ED) problem aims to minimize the total generation cost while satisfying certain constraints, such as valve-point effects, multi-fuel options, prohibited operating zones, transmission losses, and ramp rate limits. In this paper, these constraints are considered simultaneously for the first time, resulting in a complex nonconvex ED problem. A diversity-based parallel particle swarm optimization (DPPSO) is proposed to solve the nonconvex ED problem, where the implementation details—such as evaluation function design, particle definition, and equality and inequality handling strategies—have been carefully discussed. In our approach, the population of DPPSO is divided into different groups to maintain diversity in particles so that the optimization capacity can be enhanced. An asynchronous information–sharing mechanism (AISM) helps decrease the population size. Hence, the computational burden is reduced. Moreover, information in different groups is calculated parallelly and updated asynchronously to improve computational efficiency. Benchmark functions are employed to demonstrate the effectiveness of the proposed method. Furthermore, three nonconvex ED problems are resolved by the proposed method, and state-of-the-art performance has been achieved. In addition, the proposed algorithm is highly modular, making it easy to unite other salient variants of particle swarm optimization (PSO) to improve its performance.

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Section: Dppsomentioning

confidence: 99%

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Xin

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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Efficient Real-Time Path Planning with Self-Evolving Particle Swarm Optimization in Dynamic Scenarios

Xin,

Li,

Zhang

et al. 2023

Un. Sys.

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Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO’s low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here [Code and Video: https://github.com/XinJingHao/Real-time-Path-planning-with-SEPSO ].

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Optimization of Multimodal Benchmark Functions Using Fish Cooperative Hunting Behaviors

Cited by 2 publications

References 14 publications

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Efficient Real-Time Path Planning with Self-Evolving Particle Swarm Optimization in Dynamic Scenarios

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