987 research outputs found
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an
alternative way to design this kind of system based on decentralized control using distributed,
autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions
provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually
do not consider true adaptation and re-configuration. Understanding how, in nature, complex things
are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful
adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur-
ing systems. The paper provides an overview of some of the principles found in nature and biology and
analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to
solve complex engineering problems, especially in the manufacturing field. An industrial automation
case study is used to illustrate a bio-inspired method based on potential fields to dynamically route
pallets
Heuristics and Metaheuristics Approaches for Facility Layout Problems: A Survey
Facility Layout Problem (FLP) is a NP-hard problem concerned with the arrangement of facilities as to minimize the distance travelled between all pairs of facilities. Many exact and approximate approaches have been proposed with an extensive applicability to deal with this problem. This paper studies the fundamentals of some well-known heuristics and metaheuristics used in solving the FLPs. It is hoped that this paper will trigger researchers for in-depth studies in FLPs looking into more specific interest such as equal or unequal FLPs
Particle Bee Algorithm for Tower Crane Layout with Material Quantity Supply and Demand Optimization.
The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, readymixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms
The single row layout problem with clearances
The single row layout problem (SRLP) is a specially structured instance of the classical facility layout problem, especially used in flexible manufacturing systems. The SRLP consists of finding the most efficient arrangement of a given number of machines along one side of the material handling path with the purpose of minimising the total weighted sum of distances among all machine pairs. To reflect real manufacturing situations, a minimum space (so-called clearances) between machines may be required by observing technological constraints, safety considerations and regulations. This thesis intends to outline the different concepts of clearances used in literature and analyse their effects on modelling and solution approaches for the SRLP. In particular the special characteristics of sequence-dependent, asymmetric clearances are discussed and finally extended to large size clearances (machine-spanning clearances). For this, adjusted and novel model formulations and solution approaches are presented. Furthermore, a comprehensive survey of articles published in this research area since 2000 is provided which identify recent developments and emerging trends in SRLP
A Hybrid Ant Lion Optimizer (ALO) Algorithm for Construction Site Layout Optimization
A well-planned layout will contribute to saving time and site congestion as well as minimize travel distance, material handling effort, and operational cost. However, most of developed mathematical optimization procedures only work for small-scale problems and often falls into either local or global optima which do not guarantee the further convergence. Therefore, this study is motivated to propose a Hybrid Ant Lion Optimizer (ALO) algorithm inspired by ant lions’ predatory behavior, combining optimization techniques and heuristic methods to overcome a limitation of previous research. The validation has demonstrated that the proposed algorithm is able to provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence. The hybrid ALO algorithm also finds superior optimal solutions for the majority of site layout problems employed, showing that this algorithm has merits in solving constrained problems with diverse search spaces. The optimal results obtained for the site layout optimization demonstrate the applicability of the proposed algorithm in solving real problems with unknown search spaces as well
A Vitual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems
Balanced circular bin packing problems consist in positioning a given number
of weighted circles in order to minimize the radius of a circular container
while satisfying equilibrium constraints. These problems are NP-hard, highly
constrained and dimensional. This paper describes a swarm algorithm based on a
virtual-force system in order to solve balanced circular bin packing problems.
In the proposed approach, a system of forces is applied to each component
allowing to take into account the constraints and minimizing the objective
function using the fundamental principle of dynamics. The proposed algorithm is
experimented and validated on benchmarks of various balanced circular bin
packing problems with up to 300 circles. The reported results allow to assess
the effectiveness of the proposed approach compared to existing results from
the literature.Comment: 23 pages including reference
Overview of Multi-Objective Optimization Approaches in Construction Project Management
The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios
Cuckoo search algorithm for construction site layout planning
A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.publishedVersionPeer reviewe
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
The Victoria Amazonica plant, often known as the Giant Water Lily, has the
largest floating spherical leaf in the world, with a maximum leaf diameter of 3
meters. It spreads its leaves by the force of its spines and creates a large
shadow underneath, killing any plants that require sunlight. These water
tyrants use their formidable spines to compel each other to the surface and
increase their strength to grab more space from the surface. As they spread
throughout the pond or basin, with the earliest-growing leaves having more room
to grow, each leaf gains a unique size. Its flowers are transsexual and when
they bloom, Cyclocephala beetles are responsible for the pollination process,
being attracted to the scent of the female flower. After entering the flower,
the beetle becomes covered with pollen and transfers it to another flower for
fertilization. After the beetle leaves, the flower turns into a male and
changes color from white to pink. The male flower dies and sinks into the
water, releasing its seed to help create a new generation. In this paper, the
mathematical life cycle of this magnificent plant is introduced, and each leaf
and blossom are treated as a single entity. The proposed bio-inspired algorithm
is tested with 24 benchmark optimization test functions, such as Ackley, and
compared to ten other famous algorithms, including the Genetic Algorithm. The
proposed algorithm is tested on 10 optimization problems: Minimum Spanning
Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature
Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color
Quantization, and Image Segmentation and compared to traditional and
bio-inspired algorithms. Overall, the performance of the algorithm in all tasks
is satisfactory.Comment: 45 page
HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made
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