Stochastic Diffusion Search Review (original) (raw)
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Stochastic diffusion search: Partial function evaluation in swarm intelligence dynamic optimisation
Studies in Computational Intelligence, 2006
The concept of partial evaluation of fitness functions, together with mechanisms manipulating the resource allocation of population based search methods, are presented in the context of Stochastic Diffusion Search, a novel swarm intelligence metaheuristic that has many similarities with ant and evolutionary algorithms. It is demonstrated that the stochastic process ensuing from these algorithmic concepts has properties that allow the algorithm to optimise noisy fitness functions, to track moving optima, and to redistribute the population after quantitative changes in the fitness function. Empirical results are used to validate theoretical arguments.
Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results.
Deploying swarm intelligence in medical imaging
2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2014
This paper introduces a novel approach in using a swarm intelligence algorithm-Stochastic Diffusion Search-in medical imaging. After summarising the results of some previous work-showing how the algorithm assists the identification of metastasis in bone scans and microcalcifications on the mammographs-for the first time, the use of the algorithm in assessing the CT images of aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this paper is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by two senior radiologists.
Stochastic Difiusion Search: partial function evaluation in swarm intelligence dynamic optimisation
The concept of partial evaluation of fitness functions, together with mechanisms manipulating the resource allocation of population based search methods, are presented in the context of Stochastic Diffusion Search, a novel swarm intelligence metaheuristic that has many similarities with ant and evolutionary algorithms. It is demonstrated that the stochastic process ensuing from these algorithmic concepts has properties that allow the algorithm to optimise noisy fitness functions, to track moving optima, and to redistribute the population after quantitative changes in the fitness function. Empirical results are used to validate theoretical arguments.
Stabilizing Swarm Intelligence Search via Positive Feedback Resource Allocation
Studies in Computational Intelligence, 2008
A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.
Overview of Algorithms for Swarm Intelligence
Computational Collective Intelligence. Technologies and Applications, 2011
Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.
Trans. Comput. Collect. Intell., 2019
Stochastic diffusion search (SDS) is a global Swarm Intelligence optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Although population based decision mechanisms employed by many Swarm Intelligence methods can suffer poor convergence resulting in ill-defined halting criteria and loss of the best solution, as a result of its resource allocation mechanism, the solutions found by Stochastic Diffusion Search enjoy excellent stability.
Introductory Review of Swarm Intelligence Techniques
Studies in computational intelligence, 2022
With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.
An investigation into the merger of stochastic diffusion search and particle swarm optimisation
2011
This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs.