FREE SEARCH AND DIFFERENTIAL EVOLUTION TOWARDS DIMENSIONS NUMBER CHANGE

This paper presents an exploration of Free Search (FS) and modified Differential Evolution (DE) with enhanced adaptivity. The aim of the study is to identify how these methods can cope with changes of the number of variables of a hard design test, unaided. The results suggest that both methods can adapt successfully to the variation of the number of variables and constraint conditions. The results are presented. Contributions to the engineering design are replacement in high extent of human based search with machine based and movement of optimisation process from human guided to machine self guided search

HEURISTICS OPTIMISATION OF NUMERICAL FUNCTIONS

The article presents an investigation of heuristic behaviour of search algorithms applied to numerical problems. The aim is to compare the abilities of Particle Swarm Optimisation, Differential Evolution and Free Search to adapt to variety of search spaces without the need for constant re-tuning of algorithms parameters. The article focuses on several advanced characteristics of Free Search and attempts to clarify specifics of its behaviour. The achieved experimental results are presented and discussed

Adaptive intelligence: essential aspects

The article discusses essential aspects of Adaptive Intelligence. Experimental results on optimisation of global test functions by Free Search, Differential Evolution, and Particle Swarm Optimisation clarify how these methods can adapt to multi-modal landscape and space dominated by sub-optimal regions, without supervisors’ control. The achieved results are compared and analysed

Visualisation of advanced search

This article presents investigation on visualisation of search processes. Existing evolutionary and adaptive algorithms for search and optimisation, in certain extent, may differ from each other in behaviour and in obtained results. An intention for future analysis of search algorithms, their behaviour and differences motivates the development of tools for visual representation of the search process. By developing a 3D graphical interface for Computational Intelligence Software such as Free Search [1] [2], Particle Swarm Optimisation [3], Differential Evolution [4] and Genetic Algorithm [5] [6] it is possible to build a scene with test function and individuals, moving on the landscape of that test function towards their goals

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

Free Search – comparative analysis 100

Abstract: Search methods’ abilities for adaptation to various multidimensional tasks where optimisation parameters are hundreds, thousands and more, without retuning of algorithms’ parameters seems to be a great challenge for modern computational intelligence. Many evolutionary, swarm and adaptive methods, which perform well on numerical tests with up to ten dimensions are suffering insuperable stagnation when applied to 100 and more dimensional tests. This article presents a comparison between particle swarm optimisation, differential evolution both with enhanced adaptivity and Free Search applied to 100 multidimensional heterogeneous real-value numerical tests. The aim is to extend the knowledge on how high dimensionality reflects on search space complexity, in particular to identify minimal time and minimal number of objective function evaluations required by used methods for reaching acceptable solution with non-zero probability on tasks with high dimensions’ number. The achieved experimental results are summarised and analysed. Brief discussion on concepts, which support search methods effectiveness, concludes the article

Free search in multidimensional space (presentation)

Evaluation on multidimensional tests of Free Search, Differential Evolution, Particle Swarm Optimization Study abilities to avoid stagnation and trapping in local suboptimal solution Identify minimal number of iterations and time required to resolve multidimensional tasks with acceptable precisio

Adaptive intelligence - Essential aspects (This is an extended version of the article presented on the International Conference on Automatics and Informatics 2009.)

The article discusses essential aspects of Adaptive Intelligence. Experimental results on optimisation of global test functions by Free Search, Differential Evolution, and Particle Swarm Optimisation clarify how these methods can adapt to multi-modal landscape and space dominated by sub-optimal regions, without supervisors’ control. In addition Free Search separately is evaluated on hard constraint global optimisation problem with unknown solution. It illustrates Free Search abilities for adaptation to unknown space. The achieved results are compared and analysed

Free search in multidimensional space

One of the challenges for modern search methods is resolving multidimensional tasks where optimization parameters are hundreds, thousands and more. Many evolutionary, swarm and adaptive methods, which perform well on numerical test with up to 10 dimensions are suffering insuperable stagnation when are applied to the same tests extended to 50, 100 and more dimensions. This article presents an original investigation on Free Search, Differential Evolution and Particle Swarm Optimization applied to multidimensional versions of several heterogeneous real-value numerical tests. The aim is to identify how dimensionality reflects on the search space complexity, in particular to evaluate relation between tasks’ dimensions’ number and corresponding iterations’ number required by used methods for reaching acceptable solution with non-zero probability. Experimental results are presented and analyzed