Andrei Petrovski

D2mopso: Mopso based on decomposition and dominance with archiving using crowding distance in objective and solution spaces

This paper improves a recently developed multi-objective particle swarm optimizer () that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by transforming it to a set of aggregation problems, whereas dominance plays a major role in building the leaders’ archive. introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improved method is evaluated on standard benchmarks including both constrained and unconstrained test problems, by comparing it with three state of the art multi-objective evolutionary algorithms: MOEA/D, OMOPSO, and dMOPSO. The comparison and analysis of the experimental results, supported by statistical tests, indicate that the proposed algorithm is highly competitive, efficient, and applicable to a wide range of multi-objective optimization problems.

Multi-objective Optimisation of Cancer Chemotherapy Using Evolutionary Algorithms

The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result of this, a particular patient may be treated in the wrong way if the decision about the most appropriate treatment objective was inadequate. To partially alleviate this problem, we show in this paper how the multi-objective approach to chemotherapy optimisation can be used. This approach provides the oncologist with versatile treatment strategies that can be applied in ambiguous cases. However, the conflicting nature of treatment objectives and the non-linearity of some of the constraints imposed on treatment schedules make it difficult to utilise traditional methods of multi-objective optimisation. Evolutionary Algorithms (EA), on the other hand, are often seen as the most suitable method for tackling the problems exhibiting such characteristics. Our present study proves this to be true and shows that EA are capable of finding solutions undetectable by other optimisation techniques.

A novel smart multi-objective particle swarm optimisation using decomposition

A novel Smart Multi-Objective Particle Swarm Optimisation method - SDMOPSO - is presented in the paper. The method uses the decomposition approach proposed in MOEA/D, whereby a multiobjective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. The paper also introduces a novel smart approach for sharing information between particles, whereby each particle calculates a new position in advance using its neighbourhood information and shares this new information with the swarm. The results of applying SDMOPSO on five standard MOPs show that SDMOPSO is highly competitive comparing with two state-of-the-art algorithms.

Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms

This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learning (PBIL), which is an Estimation of Distribution Algorithm (EDA), and Genetic Algorithms (GAs) have been applied to the problem of finding effective chemotherapeutic treatments. To our knowledge, EDAs have been applied to fewer real world problems compared to GAs, and the aim of the present paper is to expand the application domain of this technique.We compare and analyse the performance of both algorithms and draw a conclusion as to which approach to cancer chemotherapy optimisation is more efficient and helpful in the decision-making activity led by the oncologists.

Optimising Cancer Chemotherapy Using Particle Swarm Optimisation and Genetic Algorithms

Cancer chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumours using anti-cancer drugs with the adverse toxic side-effects caused by these drugs. Some methods of computational optimisation, Genetic Algorithms in particular, have proven to be useful in helping to strike the right balance. The purpose of this paper is to study how an alternative optimisation method – Particle Swarm Optimisation – can be used to facilitate finding optimal chemotherapeutic treatments, and to compare its performance with that of Genetic Algorithms.

An application of a multivariate estimation of distribution algorithm to cancer chemotherapy

Chemotherapy treatment for cancer is a complex optimisation problem with a large number of interacting variables and constraints. A number of different heuristics have been applied to it with varying success. In this paper we expand on this by applying two estimation of distribution algorithms to the problem. One is UMDA and the other is hBOA, the first EDA using a multivariate probabilistic model to be applied to the chemotherapy problem. While instinct would lead us to predict that the more sophisticated algorithm would yield better performance on a complex problem like this, we show that it is outperformed by the algorithms using the simpler univariate model. We hypothesise that this is caused by the more sophisticated algorithm being impeded by the large number of interactions in the problem which though present, do not complicate the search for optima.

D 2 MOPSO: multi-objective particle swarm optimizer based on decomposition and dominance

D2MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leaders archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D2MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.

Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces

In [1], we introduced Smart Multi-Objective Particle Swarm Optimisation using Decomposition (SDMOPSO). The method uses the decomposition approach proposed in Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D), whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. This work customize SDMOSPO to cover binary problems and applies the proposed binary method on the channel selection problem for Brain-Computer Interfaces(BCI).

Statistical optimisation and tuning of GA factors

This paper presents a practical methodology of improving the efficiency of genetic algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successfully applied to both binary-and integer-encoded genetic algorithms that search for good chemotherapeutic schedules

Data-driven modelling of electromagnetic interferences in motor vehicles using intelligent system approaches

This paper addresses the issue of designing and building an intelligent system for automated detection and identification of inappropriate level of electromagnetic interference in motor vehicles. A conceptual framework of data-driven modelling and systematic data analysis has been proposed and piloted. The experimental results support the idea of using the suggested intelligent system for automotive fault detection in general.