Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective 'Hybrid Co-evolution based Particle Swarm Optimisation' methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method

Bat Algorithm for Multi-objective Optimisation

Engineering optimization is typically multiobjective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving nonlinear, global optimisation problems. In this paper, we extend this algorithm to solve multiobjective optimisation problems. The proposed multiobjective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417

Assessing partnership alternatives in an IT network employing analytical methods

One of the main critical success factors for the companies is their ability to build and maintain an effective collaborative network. This is more critical in the IT industry where the development of sustainable competitive advantage requires an integration of various resources, platforms, and capabilities provided by various actors. Employing such a collaborative network will dramatically change the operations management and promote flexibility and agility. Despite its importance, there is a lack of an analytical tool on collaborative network building process. In this paper, we propose an optimization model employing AHP and multiobjective programming for collaborative network building process based on two interorganizational relationships’ theories, namely, (i) transaction cost theory and (ii) resource-based view, which are representative of short-term and long-term considerations. The five different methods were employed to solve the formulation and their performances were compared. The model is implemented in an IT company who was in process of developing a large-scale enterprise resource planning (ERP) system. The results show that the collaborative network formed through this selection process was more efficient in terms of cost, time, and development speed. The framework offers novel theoretical underpinning and analytical solutions and can be used as an effective tool in selecting network alternatives

Industrial water management by multiobjective optimization: from individual to collective solution through eco-industrial parks.

Industrial water networks are designed in the first part by a multiobjective optimization strategy, where fresh water, regenerated water flow rates as well as the number of network connections (integer variables) are minimized. The problem is formulated as a Mixed-Integer Linear Programming problem (MILP) and solved by the ε-constraint method. The linearization of the problem is based on the necessary conditions of optimality defined by Savelski and Bagajewicz (2000). The approach is validated on a published example involving only one contaminant. In the second part the MILP strategy is implemented for designing an Eco-Industrial Park (EIP) involving three companies. Three scenarios are considered: EIP without regeneration unit, EIP where each company owns its regeneration unit and EIP where the three companies share regeneration unit(s). Three possible regeneration units can be chosen, and the MILP is solved under two kinds of conditions: limited or unlimited number of connections, same or different gains for each company. All these cases are compared according to the global equivalent cost expressed in fresh water and taking also into account the network complexity through the number of connections. The best EIP solution for the three companies can be determined

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Modeling Profit of Sliced 5G Networks for Advanced Network Resource Management and Slice Implementation

The core innovation in future 5G cellular networksnetwork slicing, aims at providing a flexible and efficient framework of network organization and resource management. The revolutionary network architecture based on slices, makes most of the current network cost models obsolete, as they estimate the expenditures in a static manner. In this paper, a novel methodology is proposed, in which a value chain in sliced networks is presented. Based on the proposed value chain, the profits generated by different slices are analyzed, and the task of network resource management is modeled as a multiobjective optimization problem. Setting strong assumptions, this optimization problem is analyzed starting from a simple ideal scenario. By removing the assumptions step-by-step, realistic but complex use cases are approached. Through this progressive analysis, technical challenges in slice implementation and network optimization are investigated under different scenarios. For each challenge, some potentially available solutions are suggested, and likely applications are also discussed

Trade-off Between Cost and Effectiveness of Control of Nutrient Loading into a Water Body

A system consisting of a watershed and a water body is considered, and a methodology is presented for selecting the alternative scheme offering the best compromise between economic activity in the watershed and quality of the water body. The general problem is specified for the system of a watershed and a lake endangered by eutrophication. Both economic activity and eutrophication can be characterized by several criteria. The method is applied to actual data from a subwatershed of Lake Balaton, Hungary, where the economic objective is to minimize the sum of costs and losses for the various control measures and the environmental objective is to minimize the amount of P available for algal growth. Both of these objectives are decomposed into several criteria. The action space consists of six pure strategies, namely, the control of (1) point-source pollution, (2) fertilizer, (3) erosion, (4) land use, (5) runoff control, and (6) sediment yield. These six pure actions lead to the definition of eight mixed alternatives. The phosphorus-loading portion of the model is run repeatedly with different stochastic input sequences to account for hydrologic uncertainty and the corresponding environmental objective is expressed as the probability "uj" that alternative "j" results in the largest decrease of P-loading. Model parameters are estimated using available data or published tables and graphs. Compromise programming is used to find a trade-off (or satisfactum solution) that balances the two conflicting objectives. In order to facilitate further application of the methodology, several points are discussed such as the relationship between the lake and its catchment, the error in stochastic simulation, the consideration of various uncertainties, the effect of snowmelt, and possible coupling with detailed lake eutrophication models. Finally, a step-by-step summary of the methodology is given to facilitate application of the model to other cases. Multicriterion decision-making techniques are briefly reviewed in the appendix so that cases with more than two objectives may also be approached

Multi objective optimization in charge management of micro grid based multistory carpark

Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.Web of Science117art. no. 179