Gaussian-valued particle swarm optimization

This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.The National Research Foundation (NRF) of South Africa (Grant Number 46712) and the Natural Sciences and Engineering Research Council of Canada (NSERC).http://link.springer.combookseries/5582019-10-03hj2018Computer Scienc

Preliminary study on the particle swarm optimization with the particle performance evaluation

In this paper, the novel concept of particle performance evaluation within the particle swarm optimization algorithm (PSO) is introduced. In this method the contribution of each particle to the process of obtaining the global best solution is investigated periodically. For the particle with no contribution to the global best solution over a given number of iterations the velocity calculation is changed; in the case of this presented research, in order to improve its performance towards the global trend

Forest road planning to improve tourism accessibility: a comparison of different methods applied in a real case study

Forest road planning with the available tools, e.g. PEGGER and GIS, still requires a lot of time of an expert, and the designed roads are not guaranteed to be efficient in terms of the cost or suitability of the road. In this article, we propose a novel Genetic Algorithm (GA) based method for forest road planning. To do so, each road is represented as a sequence of fixed and variable (control) points. A novel objective (fitness) function is defined based on the length, gradient, and suitability of the roads (individuals). The proposed algorithm is applied to the Arasbaran forest area and the resulted roads are compared with PEGGER-designed roads regarding length, Bachmund index, accessibility, and suitability. The results clearly show the power of the proposed GA algorithm in reducing computation time, road construction costs, and environmental impacts compared to the common road planning approaches

Chaos driven particle swarm optimization with basic particle performance evaluation – An initial study

In this paper, the novel concept of particle performance evaluation is introduced into the chaos driven particle swarm optimization algorithm (PSO). The discrete chaotic dissipative standard map is used here as a chaotic pseudorandom number generator (CPRNG). In the novel proposed particle performance evaluation method the contribution of each particle to the process of obtaining the global best solution is investigated periodically. As a reaction to the possible poor performance of a particular particle, its velocity calculation is thereafter altered. Through utilization of this approach the convergence speed and overall performance of PSO algorithm driven by CPRNG based on Dissipative map is improved. The proposed method is tested on the CEC13 benchmark set with two different dimension settings

Constructive cooperative coevolution for large-scale global optimisation

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework