Multi agent collaborative search based on Tchebycheff decomposition

This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite sub-population, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions

On Selfish Memes: culture as complex adaptive system

We present the formal definition of meme in the sense of the equivalence between memetics and the theory of cultural evolution. From the formal definition we find that culture can be seen analytically and persuade that memetic gives important role in the exploration of sociological theory, especially in the cultural studies. We show that we are not allowed to assume meme as smallest information unit in cultural evolution in general, but it is the smallest information we use on explaining cultural evolution. We construct a computational model and do simulation in advance presenting the selfish meme powerlaw distributed. The simulation result shows that the contagion of meme as well as cultural evolution is a complex adaptive system. Memetics is the system and art of importing genetics to social sciences

Structured Memetic Automation for Online Human-like Social Behavior Learning

Meme automaton is an adaptive entity that autonomously acquires an increasing level of capability and intelligence through embedded memes evolving independently or via social interactions. This paper begins a study on memetic multiagent system (MeMAS) toward human-like social agents with memetic automaton. We introduce a potentially rich meme-inspired design and operational model, with Darwin's theory of natural selection and Dawkins' notion of a meme as the principal driving forces behind interactions among agents, whereby memes form the fundamental building blocks of the agents' mind universe. To improve the efficiency and scalability of MeMAS, we propose memetic agents with structured memes in this paper. Particularly, we focus on meme selection design where the commonly used elitist strategy is further improved by assimilating the notion of like-attracts-like in the human learning. We conduct experimental study on multiple problem domains and show the performance of the proposed MeMAS on human-like social behavior

Classic and Agent-Based Evolutionary Heuristics for Shape Optimization of Rotating Discs

The article presents a metaheuristic solution for the problem of shape optimization of a rotating annular disc. Such discs are important structural components of e.g. jet engines, steam turbines or disc brakes. The design goal is to find the disc shape that would ensure its maximal carrying capacity (corresponding to the speed of rotation), which is a variational problem with the objective functional defined by L-infinity norm. Such a definition makes the problem impossible to solve using analytical methods so utilization of metaheuristics is necessary. We present different algorithms to solve the problem starting with a classic evolutionary one, followed by agent-based and hybrid agent-based memetic algorithms, which are the main focus of this paper. The reason for this is that agent-based computing systems proved to be versatile as an optimization technique being especially efficient for the problems with complex fitness functions. The obtained experimental results encourage further application of such an approach to similar engineering problems