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Many meta-heuristics methods are applied to guide the exploration and exploitation of the search space for large scale optimization problems. These problems have attracted much attention from researchers who proposed developed a variety of techniques for locating the optimal solutions. Cultural Algorithm has been recently adopted to solve global numerical optimization problems. In this paper, a modified...
The minimum weight dominating set (MWDS) problem is a classic NP-Hard optimisation problem with a wide range of practical applications. As a result, many algorithms have been proposed for this problem. Several greedy and approximation algorithms exist which provide good results for unit disk graphs with smooth weights. However, these algorithms do not perform well when applied to general graphs. There...
Multi-population cultural algorithms are cultural evolutionary frameworks involving multiple independently evolving subpopulations. Artifact selection involves the ability of agents to autonomously reason about selecting artifacts towards achieving their goals. In this study, agent migration between populations in a multi-population cultural algorithm is explored as an approach for augmenting artifact...
Dynamic Heterogeneous Multi-Population Cultural Algorithm (D-HMP-CA) is a novel optimization algorithm which presents an effective as well as efficient performance to solve large scale global optimization problems. It incorporates dynamic decomposition techniques in order to divide problem dimensions among its local CAs. The variable interactions is not considered in the incorporated dynamic decomposition...
The EigenAnt Ant Colony System (EAAS) model is an Ant Colony Optimization (ACO) model based on the EigenAnt algorithm. In previous work, EAAS was found to perform competitively with the Enhanced Ant Colony System (EACS) algorithm, a state-of-the-art method for the Sequential Ordering Problem (SOP). In this paper, we extend EAAS by increasing the amount of stochasticity in its solution construction...
This paper presents a path-planning approach to enable a swarm of robots move to a goal region while avoiding collisions with static and dynamic obstacles. To provide scalability and account for the complexity of the interactions in the swarm, the proposed approach combines probabilistic roadmaps with potential fields. The underlying idea is to provide the swarm with a series of intermediate goals...
Ant Colony Optimization (ACO) is a swarm intelligence technique often applied to find solutions to hard optimization problems. In this paper, we present a new decentralized peer-to-peer approach for implementing ACO on distributed memory clusters. In addition, the approach is augmented with a fuzzy logic controller to reactively adapt several parameters of the ACO as a method of offsetting the increased...
In general, the Cooperative Coevolutionary Algorithms based on separability have shown good performance when solving high dimensional optimization problems. However, the number of function evaluations required for the decomposition stage of these algorithms can growth very fast, and depends on the dimensionality of the problem. In cases where a single function evaluation is computationally expensive...
This paper proposes a path-planning approach to enable a team of unmanned aerial vehicles (UAVs) to efficiently conduct surveillance of sensitive areas. The proposed approach, termed PARCov (Planner for Autonomous Risk-sensitive Coverage), seeks to maximize the area covered by the sensors mounted on each UAV while maintaining high sensor data quality and minimizing detection risk. PARCov leverages...
Dynamic multi-objective optimisation problems have more than one objective with at least one objective that changes over time. Previous studies indicated that different knowledge sharing strategies increase the performance of the dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm in different dynamic environments. Therefore, this paper investigates the performance of the DVEPSO...
The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results...
Numerous algorithms have been invented for optimizations which are nature inspired and based on real life behaviour of species. In this paper, intelligent chasing and hunting methods adopted by the dogs to chase and hunt their prey in groups are used to develop the novel methodology named as “Dog Group Wild Chase and Hunt Drive (DGWCHD) Algorithm”. The proposed algorithm has been implemented on some...
In this paper we present an approach to designing swarms of autonomous, adaptive robots. An observer/controller framework that has been developed as part of the Organic Computing initiative provides the architectural foundation for the individuals' adaptivity. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, it empowers the individuals to...
In this paper, we propose the idea of hybrid cooperative co-evolution (hCC). In CC, multiple instances of the same evolutionary algorithm work in parallel, each optimizes a different subset of the problem in hand. In recent years, different approaches have been introduced to divide the problem variables into separate groups based on the property of separability. The idea is that when dependent variables...
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