Particle swarm with speciation and adaptation in a dynamic environment

Li, Xiaodong; Branke, Jürgen; Blackwell, Tim

doi:10.1145/1143997.1144005

Cited by 107 publications

(87 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both algorithms were improved by re-initializing the worst performing swarm whenever all swarms converge for maintaining diversity [164]. Li et al [150] integrated the quantum particles (from mQSO) to the SPSO to improve its adaptation capabilities. Thereafter, both SPSO and mQSO were further improved by converting particles to quantum particles for a single iteration after a dynamic change is detected [146].…”

Section: Maintaining Diversity During Executionmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

View full text Add to dashboard Cite

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

show abstract

Section: Maintaining Diversity During Executionmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

492

213

View full text Add to dashboard Cite

show abstract

“…Further studies may focus on applying the mini-Swarm on various optimization problems, including dynamic [36], multi-objective [13], or multi-constraint [11] cases, etc., and investigating various operators suitable for those problems, which may also be used by some autonomous agents and multiagent systems. Moreover, it is also interesting to study characteristics of swarm algorithms under various network topologies [40].…”

Section: Discussionmentioning

confidence: 99%

A Mini-Swarm for the Quadratic Knapsack Problem

Xie

Liu

2007

2007 IEEE Swarm Intelligence Symposium

View full text Add to dashboard Cite

Abstract-The 0-1 quadratic knapsack problem (QKP) is a hard computational problem, which is a generalization of the knapsack problem (KP). In this paper, a mini-Swarm system is presented. Each agent, realized with minor declarative knowledge and simple behavioral rules, searches on a structural landscape of the problem through the guided generate-and-test behavior under the law of socially biased individual learning, and cooperates with others by indirect interactions. The formal decomposition of behaviors allows understanding and reusing elemental operators, while utilizes the heuristic information on the landscape. The results on a collection of the QKP instances by mini-Swarm versions are compared with that of both a branch-and-bound algorithm and a greedy genetic algorithm, which show its effectiveness. I. INTRO DUCTIONThe 0-1 quadratic knapsack problem (QKP) was introduced by Gallo et al. [20], which consists in choosing elements from n items for maximizing a quadratic profit objective function subject to a linear capacity constraint. The QKP is a generalization of the 0-1 knapsack problem (KP) [38], by restricting all the quadratic coefficients to zero. The knapsack problems [38] have been intensively studied due to both its theoretical interest and its wide practical applicability. Due to its generality, the QKP has more practically applications [3,20], moreover, several graph problems can be formulated as the QKP [4,45].The KP is NP-hard, although it can be solved exactly in pseudo-polynomial time through dynamic programming based on Bellman recursion [21]. As a generalized version, the QKP is much harder than the KP [10,45], where the graph structure associated with coefficients of objective function plays a important role in its solution [46].Due to the NP-hardness in strong sense, the research efforts on solving the QKP may focus on exact methods, approximate algorithms and heuristic methods.Classical The studies on approximate algorithms, which seek guaranteed polynomial-time performance on approximate solution, were only limited on some special cases of the QKP, such as the classical KP [30], and the QKP where the underlying graph is edge series-parallel [46], etc.The heuristic methods arise from practical interest, which try to find good solution in high probability with acceptable computational efforts by considering certain imprecise knowledge on the structure of the problem. The linearization and exchange (LEX) heuristic [24] uses the best linear L2-approximation to build an associated linear KP and use it for determining items in a greedy way to form a good initial solution, which is then improved by an exchange method between certain items. Hua et al. [29] studied on the convex QKP by an approximate dynamic programming approach. Julstrom [32] also illustrated the capability of several heuristics, including greedy, genetic and greedy genetic algorithms on the QKP.Swarm algorithms [7,16,34] address on tackling a specified optimization task by multiple interacting agents, where each agent works u...

show abstract

“…Li et al [18] improved the speciation algorithm of Parrott and Li [25], by introducing ideas from Blackwell and Branke [2], namely quantum particles to increase diversity, and anti-convergence to detect stagnation and subsequently reinitialize the worst performing populations. This algorithm is called Speciation-based PSO (SPSO).…”

Section: Related Workmentioning

confidence: 99%