Using Stigmergy to Solve Numerical Optimization Problems

The current methodology for designing highly efficient technological systems needs to choose the best combination of the parameters that affect the performance. In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution -- one of the leading stochastic method for numerical optimization -- in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise

A road towards the photonic hardware implementation of artificial cognitive circuits

Many technologies we use are inspired by nature. This happens in different domains, ranging from mechanics to optics to computer sciences. Nature has incredible potentialities that man still does not know or that he striving to learn through experience. These potentialities concern the ability to solve complex problems through approaches of various types of distributed intelligence. In fact, there are forms of intelligence in nature that differ from that of man, but are nevertheless exceedingly efficient. Man has often used as a model those forms of distributed intelligence that allow colonies of animals to develop places of housing or collective behaviors of extreme complexity. Recently, M. Alonzo et alii (Sci.Rep. 8, 5716 (2018)) published a hardware implementation to solve complex routing problems in modern information networks by exploiting the immense possibilities offered by light. This article presents an addressable photonic circuit based on the decision-making processes of ant colonies looking for food. When ants search for food, they modify their surroundings by leaving traces of pheromone, which may be reinforced and function as a type of path marker for when food has been found. This process is based on stigmergy, or the modification of the environment to implement distributed decision-making processes. The photonic hardware implementation that this work proposes is a photonic X-junction that simulates this stigmergic procedure. The experimental implementation is based on the use of non-linear substrates, i.e. materials that can be modified by light, simulating the modification induced by the ants on the surrounding environment when they leave the pheromone traces. Here, two laser beams generate two crossing channels in which the index of refraction is increased with respect to the whole substrate. These channels act as integrated waveguides (almost self-written optical fibers) within which optical information can be propagated (as happens for the ants that follow traces of pheromone already “written”). The proposed device is a X-junction with two crossing waveguides, whose refractive index contrast is defined by the intensities of the writing light beams. The higher the writing intensity, the greater the induced index variation, as if it were an increasingly intense pheromone trace. The information will follow the most contrasted harm of the junction, which is driven and eventually switched by the writing light intensity. Any optical information that will be sent to the device will follow the most intense trace, i.e. the most contrasted waveguide. The paper demonstrates a device that can be wholly operated using the light and that can be the basis of complex hardware configurations that might reproduce the stigmergic distributed intelligence. This is a highly significant innovation in the field of electronic and photonic technologies, within which artificial cognition and decision processes are implemented into a hardware circuit and not in a software code

All-Optical Reinforcement Learning in Solitonic X-Junctions

L'etologia ha dimostrato che gruppi di animali o colonie possono eseguire calcoli complessi distribuendo semplici processi decisionali ai membri del gruppo. Ad esempio, le colonie di formiche possono ottimizzare le traiettorie verso il cibo eseguendo sia un rinforzo (o una cancellazione) delle tracce di feromone sia spostarsi da una traiettoria ad un'altra con feromone più forte. Questa procedura delle formiche possono essere implementati in un hardware fotonico per riprodurre l'elaborazione del segnale stigmergico. Presentiamo qui innovative giunzioni a X completamente integrate realizzate utilizzando guide d'onda solitoniche in grado di fornire entrambi i processi decisionali delle formiche. Le giunzioni a X proposte possono passare da comportamenti simmetrici (50/50) ad asimmetrici (80/20) utilizzando feedback ottici, cancellando i canali di uscita inutilizzati o rinforzando quelli usati.Ethology has shown that animal groups or colonies can perform complex calculation distributing simple decision-making processes to the group members. For example ant colonies can optimize the trajectories towards the food by performing both a reinforcement (or a cancellation) of the pheromone traces and a switch from one path to another with stronger pheromone. Such ant's processes can be implemented in a photonic hardware to reproduce stigmergic signal processing. We present innovative, completely integrated X-junctions realized using solitonic waveguides which can provide both ant's decision-making processes. The proposed X-junctions can switch from symmetric (50/50) to asymmetric behaviors (80/20) using optical feedbacks, vanishing unused output channels or reinforcing the used ones

SamACO: variable sampling ant colony optimization algorithm for continuous optimization

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants’ solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising

Learning Sensitive Stigmergic Agents for Solving Complex Problems

Systems composed of several interacting autonomous agents have a huge potential to efficiently address complex real-world problems. Usually agents communicate by directly exchanging information and knowledge about the environment. The aim of the paper is to develop a new computational model that endows agents with a supplementary interaction/search mechanism of stigmergic nature. Multi-agent systems can therefore become powerful techniques for addressing NP-hard combinatorial optimization problems. In the proposed approach, agents are able to indirectly communicate by producing and being influenced by pheromone trails. Each stigmergic agent is characterized by a certain level of sensitivity to the pheromone trails. The non-uniform pheromone sensitivity allows various types of reactions to a changing environment. For efficient search diversification and intensification, agents can learn to modify their sensitivity level according to environment characteristics and previous experience. The resulting system for solving complex problems is called Learning Sensitive Agent System (LSAS). The proposed LSAS model is used for solving several NP-hard problems such as the Asymmetric and Generalized Traveling Salesman Problems. Numerical experiments indicate the robustness and the potential of the new metaheuristic

Uncertainty And Evolutionary Optimization: A Novel Approach

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201