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

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 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

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’

Swarmic paintings and colour attention

Swarm-based multi-agent systems have been deployed in non-photorealistic rendering for many years. This paper introduces a novel approach in adapting a swarm intelligence algorithm – Stochastic Diffusion Search – for producing non-photorealistic images. The swarm-based system is presented with a digital image and the agents move throughout the digital canvas in an attempt to satisfy the dynamic roles – attention to different colours - associated to them via their fitness function. Having associated the rendering process with the concepts of ‘attention’ in general and colour attention in particular, this papers briefly discusses the ‘computational creativity’ of the work through two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence’s two infamous phases of exploration and exploitation

Differences in human plasma protein interactions between various polymersomes and stealth liposomes as observed by fluorescence correlation spectroscopy

A significant factor hindering the clinical translation of polymersomes as vesicular nanocarriers is the limited availability of comparative studies detailing their interaction with blood plasma proteins compared to liposomes. Here, polymersomes are self-assembled via film rehydration, solvent exchange, and polymerization-induced self-assembly using five different block copolymers. The hydrophilic blocks are composed of anti-fouling polymers, poly(ethylene glycol) (PEG) or poly(2-methyl-2-oxazoline) (PMOXA), and all the data is benchmarked to PEGylated “stealth” liposomes. High colloidal stability in human plasma (HP) is confirmed for all but two tested nanovesicles. In situ fluorescence correlation spectroscopy measurements are then performed after incubating unlabeled nanovesicles with fluorescently labeled HP or the specific labeled plasma proteins, human serum albumin, and clusterin (apolipoprotein J). The binding of HP to PMOXA-polymersomes could explain their relatively short circulation times found previously. In contrast, PEGylated liposomes also interact with HP but accumulate high levels of clusterin, providing them with their known prolonged circulation time. The absence of significant protein binding for most PEG-polymersomes indicates mechanistic differences in protein interactions and associated downstream effects, such as cell uptake and circulation time, compared to PEGylated liposomes. These are key observations for bringing polymersomes closer to clinical translation and highlighting the importance of such comparative studies

An efficient chameleon swarm algorithm for economic load dispatch problem

Economic Load Dispatch (ELD) is a complicated and demanding problem for power engineers. ELD relates to the minimization of the economic cost of production, thereby allocating the produced power by each unit in the most possible economic manner. In recent years, emphasis has been laid on minimization of emissions, in addition to cost, resulting in the Combined Economic and Emission Dispatch (CEED) problem. The solutions of the ELD and CEED problems are mostly dominated by metaheuristics. The performance of the Chameleon Swarm Algorithm (CSA) for solving the ELD problem was tested in this work. CSA mimics the hunting and food searching mechanism of chameleons. This algorithm takes into account the dynamics of food hunting of the chameleon on trees, deserts, and near swamps. The performance of the aforementioned algorithm was compared with a number of advanced algorithms in solving the ELD and CEED problems, such as Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Earth Worm Algorithm (EWA). The simulated results established the efficacy of the proposed CSA algorithm. The power mismatch factor is the main item in ELD problems. The best value of this factor must tend to nearly zero. The CSA algorithm achieves the best power mismatch values of 3.16×10−13, 4.16×10−12 and 1.28×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the ELD problem. The CSA algorithm achieves the best power mismatch values of 6.41×10−13 , 8.92×10−13 and 1.68×10−12 for demand loads of 700, 1000, and 1200 MW, respectively, of the CEED problem. Thus, the CSA algorithm was found to be superior to the algorithms compared in this work

Performa Pertumbuhan Ikan Kerapu Bebek Cromileptes Altivelis Yang Dibudidayakan Dengan Sistem Keramba

Humpback grouper Cromileptes altivelis is seawater that lives in tropical waters, especially in Indonesia. This fish has high economic value and is the most expensive grouper fish in Indonesia. Humpback grouper seeds can be used as ornamental fish or raised for consumption fish. This study aimed to evaluate the growth performance of humpback grouper cultivated with the cage system. During the maintenance, fish were fed with 1-5% feed ratio. Fish were reared from 30g to 200g in size, and a feed conversion ratio of 2 was obtained with a survival rate of 94%. The fish were started to be given mixed feed with a 3-4% feed ratio, consisting of 15% pellets and 85% fresh fish which had been cut according to the fish mouth opening. The next maintenance starts from fish measuring 200g to reaching 500g with a survival rate of 93%. The profit obtained from harvesting 12 times in one year is IDR 590 587 187 years-1, R/C Ratio 1.80, the payback period is 1.7 years, break event point IDR 376 100 576, BEP unit 895kg, and price cost of production (HPP) Rp 233 312kg-1.Ikan kerapu bebek Cromileptes altivelis merupakan ikan air laut yang hidup di perairan tropis khususnya Indonesia. Ikan ini memiliki nilai ekonomis tinggi dan merupakan jenis ikan kerapu termahal di Indonesia.  Benih ikan kerapu bebek dapat digunakan sebagai ikan hias maupun dibesarkan menjadi ikan konsumsi. Tujuan dari studi ini adalah untuk mengevaluasi performa pertumbuhan ikan kerapu bebek yang dibudidayakan dengan sistem keramba. Selama pemeliharaan ikan diberi pakan dengan tingkat pemberian pakan 1-5%.  Ikan dipelihara mulai ukuran 30g hingga berukuran 200g, dan diperoleh rasio konversi pakan 2 dengan sintasan 94%.  Pada saat mencapai ukuran 200g, ikan diberi pakan campuran dengan tingkat pemberian pakan 3-4% yang terdiri dari pelet 15% dan ikan segar 85% yang telah dipotong-potong sesuai bukaan mulut ikan.  Pemeliharaan selanjutnya dilakukan selama 10-11 bulan, sampai mencapai ukuran 500g dengan sintasan 93%. Keuntungan yang didapat dari panen yang dilakukan sebanyak 12 kali dalam satu tahun sebesar Rp590 587 187 tahun-1, R/C Rasio 1,80, payback periode selama 1,7 tahun, break event point Rp376 100 576, BEP unit 895kg, dan harga pokok produksi (HPP) Rp 233 312kg-1