Collaborative hardware/software partition of coarse-grained reconfigurable system using evolutionary ant colony optimization

Ant Colony Optimization (ACO) is a popular meta-heuristic for solving combinatorial optimization problems. ACO uses the concept of ants foraging for food to find good solutions to these types of problems. ACO has been successfully applied to many problems, from the traveling salesman problem (TSP), to the problem of network routing. However, it has been pointed out that ACO does not perform as well as other heuristics in very dynamic problems. At first, this statement seems strange but a close look reveals that the nature strategy that inspires the ACO meta-heuristic has an important element that is lacking in ACO: evolution. This paper proposes a new algorithm, named Evolutionary Ant Colony Optimization (EACO), that combines ACO with elements of traditional Genetic Algorithms (GA), namely: selection, recombination, and mutation. Individual ants are endowed with a genotype that is allowed to evolve through generations of the population. In doing this, the EACO algorithm adds another element of optimization to the ACO algorithm that allows the individual agents (ants) in the algorithm to improve their behavior over several generations. Our results demonstrate that EACO can indeed overcome the hurdles faced by the original ACO.KeywordsGenetic AlgorithmShort PathTravel Salesman ProblemTravel Salesman ProblemSwarm IntelligenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

In this paper, a hybrid algorithm is proposed for travelling salesman problem (TSP). TSP, one of the vehicle route planning problems, is to minimize the cost of travel of a salesman in visiting all the cities in a given set, and return to the starting city. Basically, the proposed algorithm combines ant colony optimization (ACO) with genetic algorithm (GA) and can explore and exploit search spaces. It has both the advantage of ACO, the ability to find feasible solutions and to avoid premature convergence, and that of GA, the ability to avoid being trapped in local optima. The test results show that proposed algorithm finds optimum solutions effectively.

Time series importance measure-based reliability optimization for cellular manufacturing systems

This paper proposes a distributed multi-agent framework for discovering and optimizing evacuation routes on demand. Our framework assumes mobile ad hoc networks (MANETs) composed of smartphones with geo-location capabilities. On the network, heterogeneous mobile agents cooperatively insert knowledge about crowd in our mass evacuation framework. They are relying exclusively on crowd sourcing; therefore our framework is layout independent and adaptable for any situation. The mobile agents take advantage of ant colony optimization (ACO) in order to collect such knowledge. Once users reach safe areas, they distribute agents to inform the directions of the locations of the safe areas. On the other hand, evacuating users distribute agents to search safe areas, based on guidance given by the agents from the safe areas. Once each searching agent reaches the safe area, it traces its path backwardly collecting geographical information of intermediate nodes for composing an evacuation route. During the backward travel, agents lay down pheromone as they migrate back based on the ACO algorithm, strengthening quasi-optimal physical routes, and hence guiding succeeding agents. A characteristic of pheromone in this family of algorithms is that it lessens during run-time, keeping the information about successful escape routes current, as is essential in an evacuation scenario. We have implemented a simulator based on our framework in order to show the effectiveness of our technique. We discuss the behaviors of our system with various settings on the simulator for real world implementation in the near future.

We introduce a re-configurable system design for positron emission tomography (PET) for purpose of maximizing the cost effectiveness of PET. In this new design concept, a PET system employs a small number of compact, flat-panel (cFP) PET detectors (which we believe will be made available in near future by us or others) to assume various scanner geometries so that the performance properties desired in various imaging applications can be met by a single PET system. For purpose of demonstration, we consider the design of a re-configurable small-animal system consisting of four cFP detectors that are approximately 8×8 cm2 in size and made of 10mm thick LSO. We consider four geometries for the scanner and conduct Monte-Carlo simulations to study their sensitivity, scatter fractions, random fractions, and noise-equivalent count rates. Our results indicate that, in terms of sensitivity and imaging field-of-view (FOV) the re-configurable system that we consider are very promising for small-animal imaging.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithms. The main idea is to use the Differential Evolution (DE) algorithm for a good starting point for Ant Colony Optimization (ACO) algorithm. A benchmark of six well-known test functions is employed to check the performances of the proposed approach in terms of convergence rate, quality of optimum solutions and computing time. The results obtained show that the performances of the hybrid algorithm outperform significantly the DE and ACO algorithms.

Optical systems are extensively utilized by industry government and military organizations. The conceptual design engineering design fabrication and testing of these systems presently requires significant time typically on the order of 3-5 years. The Knowledge-Based Environment for Optical System Design (KB-OSD) Program has as its principal objectives the development of a methodology and tool(s) that will make a notable reduction in the development time of optical system projects reduce technical risk and overall cost. KB-OSD can be considered as a computer-based optical design associate for system engineers and design engineers. By utilizing artificial intelligence technology coupled with extensive design/evaluation computer application programs and knowledge bases the KB-OSD will provide the user with assistance and guidance to accomplish such activities as (i) develop system level and hardware level requirements from mission requirements (ii) formulate conceptual designs (iii) construct a statement of work for an RFP (iv) develop engineering level designs (v) evaluate an existing design and (vi) explore the sensitivity of a system to changing scenarios. The KB-OSD comprises a variety of computer platforms including a Stardent Titan supercomputer numerous design programs (lens design coating design thermal materials structural atmospherics etc. ) data bases and heuristic knowledge bases. An important element of the KB-OSD Program is the inclusion of the knowledge of individual experts in various areas of optics and optical system engineering. This knowledge is obtained by KB-OSD knowledge engineers performing

Many industrial and academic optimization problems have successfully been tackled with ant colony optimization (ACO), but the ability of ACO to deal with hard constraints has not yet been investigated widely. Most real-world applications, such as fleet routing, rostering and timetabling, however, are subject to hard constraints. Extensions of ACO with hard constraint handling are thus a worthwhile field of study. Constraint handling in meta-heuristics has mostly been studied in the context of evolutionary algorithms. Many of the techniques proposed are penalty-based. A difficulty with such methods is the correct adjustment of penalty factors. Recently a different approach, termed stochastic ranking (SR), has been suggested which does not use a penalty formulation. Benchmarks indicate that SR is currently among the best performing methods for evolutionary constraint optimization. We discuss how the ideas underlying SR can be used in ACO and evaluate two different ways to achieve this integration: one derived from ACS (ACSsr) and one based on ranked ant system (ASRsr). We compare the performance of these to penalty-based ACO as well as to CPACS, an ACO algorithm hybridized with explicit constraint propagation. Initial benchmark results are encouraging.

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.

Focusing on system-level design and verification techniques, this text allows readers to immediately grasp concepts and put them into practice. It starts with an overview of reconfigurable computing architectures and platforms and demonstrates how to develop reconfigurable systems. This sets up the discussion of the hardware, software, and system techniques that form the core of the text. The authors classify design and verification techniques into primary and secondary categories, allowing the appropriate ones to be easily located and compared. The techniques discussed range from system modeling and system-level design to co-simulation and formal verification. Case studies illustrate real-world applications. Multi Copy

: As a method of reducing design and development costs for data processing systems (hardware and software) a total system design concept is proposed. The concept includes a Reconfigurable System Design Facility (RCSDF) where total system design alternatives can be emulated for the purpose of evaluating and proving designs prior to actual development. (Author)

This paper is dedicated to the problem of learning finite-state machines (FSMs), which plays a key role in automata-based programming. Metaheuristic algorithms commonly applied to this problem often use FSM mutations (small changes in the FSM structure) for solution construction. Most of them do not employ the specifics of FSMs in their work. We propose a new simple method for improving performance of these algorithms. The basic idea is to mark those transitions of FSMs that were used during fitness evaluation. Then, if a FSM mutation changes a transition that was not used in fitness evaluation, the fitness function value need not be calculated for the mutated FSM. This observation allows to conserve fitness evaluations, which often have high computational costs. The proposed method has been incorporated into several traditional and recent FSM learning algorithms based on evolutionary strategies, genetic algorithms and ant colony optimization. Experimental results are reported showing that the new method significantly improves performance of two methods based on evolutionary strategies and ant colony optimization.

In this paper, we propose a hybrid multi-cell tracking approach to accurately and jointly estimate the state and its contour of each cell. Our approach consists of level set evolution and ant colony optimization, representing, respectively, the deterministic and stochastic methods for cell tracking. Firstly, birth ants are directly distributed into the regions depicted by raw curves achieved by the traditional level set evolution. Then, the ants move towards potential regions based on the pheromone deposited by ants and the gradient information of current image. Finally, the resulting pheromone field is embedded in the variational level set to drive the evolution of cell curve to yield an accurate one and correspond cell position estimate. The experiment results show that our method could automatically track multi-cell and achieve an accurate contour estimation of each cell.