Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

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

Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

Discovering Regression Rules with Ant Colony Optimization

The majority of Ant Colony Optimization (ACO) algorithms for data mining have dealt with classification or clustering problems. Regression remains an unexplored research area to the best of our knowledge. This paper proposes a new ACO algorithm that generates regression rules for data mining applications. The new algorithm combines components from an existing deterministic (greedy) separate and conquer algorithm—employing the same quality metrics and continuous attribute processing techniques—allowing a comparison of the two. The new algorithm has been shown to decrease the relative root mean square error when compared to the greedy algorithm. Additionally a different approach to handling continuous attributes was investigated showing further improvements were possible

A new sequential covering strategy for inducing classification rules with ant colony algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms

Production quantity estimation using an improved artificial neural network

By considering on the competitive market today, managing inventory becomes one factor that affected in improving business performance. This encouraged most industries to manage it efficiently by determining effective decision for inventory replenishment. For instance, mostly, industries decide next inventory replenishment by considering on their last historical production. However, this decision cannot be implemented on the next production due to uncertainty/fluctuated condition. Therefore, poor decision on producing product will influence the business’ costs. Hence, this research proposes model based on Neural Network Back Propagation (NNBP) to estimate production quantity. This model is designed based on input variables that affect the determination of production quantity which include demand, setup costs, production, material costs, holding costs, transportation costs. The performance of NNBP can be analyzed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In order to increase the performance of NNBP, optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being hybrid with the ANN model to become Hybrid Neural Network Genetic Algorithm (HNNGA) model and Hybrid Neural Network Particle Swarm Optimization (HNNPSO) model respectively. These techniques were used to optimize attribute weighting on NNBP model. The proposed models were examined using private dataset that collected from Iron Casting Manufacturing in Klaten, Indonesia. Moreover, validation is conducted for all proposed models through both Cross-Validation and statistical analysis. The cross-validation is common technique used to prevent over fitting problem by dividing the data into two categories namely data training and data test. Meanwhile, statistical analysis considers normality test on error estimation and the significant difference among the proposed models. Experimental result shows that HNNGA and HNNPSO provide smaller measurement error that concurrently improves the performance of NNBP model. In this work, the proposed model contributes not only to update the original instrument, but also applicable and beneficial for industry, particularly in deciding effective inventory replenishment decision on production quantity

Penerapan Particle Swarm Optimization Pada Algoritma Naïve Bayes Untuk Klasifikasi Hasil Belajar

Advances in industrial technology 4.0 provide many changes in today's life. One example attached to the advancement of industrial technology 4.0 is the use of communication, transactions, even to the level of education using the acceleration of information technology. Some sectors have used advances in information technology such as the government sector, the industrial sector and even the world of education. This is because the greater the influence of the use of information technology to accelerate the transformation of each sector that uses it. But on the other hand, advances in information technology, apart from having a positive impact, also have a negative impact. As a real example of the positive impact, namely in the education sector, during the COVID-19 pandemic, the use of technology could be felt, as far as learning could be close to existing learning videos. However, one of the challenges is that advances in information technology also have a negative impact in the field of education, namely the dependence of students or students to spend more time playing online games (e-sports). So that it can affect the results of student learning achievement. Therefore, the research method that will be used is to use the classification of the influence of e-sports on student learning outcomes using the Nave Bayes algorithm which is optimized using particle swarm optimization. In its implementation, it was found that some students experienced decreased learning outcomes and some increased, of course this was influenced by several factors, both internal and external to the students themselves. The implementation of the algorithm used in this study obtained a sufficient level of classification with an AUC (area under classification) value of 0.792 and an accuracy value of 75.95

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules)-i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines and the cAnt-MinerPB producing ordered rules are also presented