Free Search and Particle Swarm Optimisation applied to Non-constrained Test

This article presents an evaluation of Particle Swarm Optimisation (PSO) with variable inertia weight and Free Search (FS) with variable neighbour space applied to nonconstrained numerical test. The objectives are to assess how high convergence speed reflects on adaptation to various test problems and to identify possible balance between convergence speed and adaptation, which allows the algorithms to complete successfully the process of search on heterogeneous tasks with limited computational resources within a reasonable finite time and with acceptable for engineering purposes precision. Modification strategies of both algorithms are compared in terms of their ability for search space exploration. Five numerical tests are explored. Achieved experimental results are presented and analysed

Adaptive intelligence applied to numerical optimisation

The article presents modification strategies theoretical comparison and experimental results achieved by adaptive heuristics applied to numerical optimisation of several non-constraint test functions. The aims of the study are to identify and compare how adaptive search heuristics behave within heterogeneous search space without retuning of the search parameters. The achieved results are summarised and analysed, which could be used for comparison to other methods and further investigation

Algorithms Applied to Global Optimisation – Visual Evaluation

Evaluation and assessment of various search and optimisation algorithms is subject of large research efforts. Particular interest of this study is global optimisation and presented approach is based on observation and visual evaluation of Real-Coded Genetic Algorithm, Particle Swarm Optimisation, Differential Evolution and Free Search, which are briefly described and used for experiments. 3D graphical views, generated by visualisation tool VOTASA, illustrate essential aspects of global search process such as divergence, convergence, dependence on initialisation and utilisation of accidental events. Discussion on potential benefits of visual analysis, supported with numerical results, which could be used for comparative assessment of other methods and directions for further research conclude presented study

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

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Particle swarm optimisation algorithms and their application to controller design for flexible structure systems

Particle swarm optimisation (PSO) is one of the relatively new optimisation techniques, which has become increasingly popular in tuning and designing controllers for different applications. A major problem is that simple PSO have a tendency to converge to local optima, mainly, due to lack of diversity in the particles as the algorithm proceeds and improper selection of other parameters. Maintaining diversity within a population is challenging for PSO, especially for dynamic problems. In order to increase diversity in the search space and to improve convergence, a new variant of PSO is proposed. The increased interest from industry and real-world applications has led to several modifications in the conventional algorithms so as to deal with multiple conflicting objectives and constraints. A modified multi-objective PSO (MOPSO) proposal is made which will allow the algorithm to deal with multi-objective optimisation problems. The main challenge, in designing a MOPSO algorithm, is to select local and global best for each particle so as to obtain a wide range of solutions that trade-off among the conflicting objectives. In the proposed algorithm, a new technique is introduced that combines external archive and non-dominated fronts of the current population in order to select the global best for each particle. The effectiveness of the proposed algorithm is assessed with two examples in controller design for vibration control of flexible structure systems and satisfactory results have been obtained