Simple Population Replacement Strategies for a Steady-State Multi-objective Evolutionary Algorithm

This paper explores some simple evolutionary strategies for an elitist, steady-state Pareto-based multi-objective evolutionary algorithm. The experimental framework is based on the SEAMO algorithm which differs from other approaches in its reliance on simple population replacement strategies, rather than sophisticated selection mechanisms. The paper demonstrates that excellent results can be obtained without the need for dominance rankings or global fitness calculations. Furthermore, the experimental results clearly indicate which of the population replacement techniques are the most effective, and these are then combined to produce an improved version of the SEAMO algorithm. Further experiments indicate the approach is competitive with other state-of-the-art multi-objective evolutionary algorithms

Dominance Based Crossover Operator for Evolutionary Multi-objective Algorithms

In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research field, there has been few trials to adapt the general variation operators to the particular context of the quest for the Pareto-optimal set. The only exceptions are some mating restrictions that take in account the distance between the potential mates - but contradictory conclusions have been reported. This paper introduces a particular mating restriction for Evolutionary Multi-objective Algorithms, based on the Pareto dominance relation: the partner of a non-dominated individual will be preferably chosen among the individuals of the population that it dominates. Coupled with the BLX crossover operator, two different ways of generating offspring are proposed. This recombination scheme is validated within the well-known NSGA-II framework on three bi-objective benchmark problems and one real-world bi-objective constrained optimization problem. An acceleration of the progress of the population toward the Pareto set is observed on all problems

The trade off between diversity and quality for multi-objective workforce scheduling

In this paper we investigate and compare multi-objective and weighted single objective approaches to a real world workforce scheduling problem. For this difficult problem we consider the trade off in solution quality versus population diversity, for different sets of fixed objective weights. Our real-world workforce scheduling problem consists of assigning resources with the appropriate skills to geographically dispersed task locations while satisfying time window constraints. The problem is NP-Hard and contains the Resource Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate a genetic algorithm and serial schedule generation scheme together with various multi-objective approaches. We show that multi-objective genetic algorithms can create solutions whose fitness is within 2% of genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. The result is highly significant for complex real-world problems where objective weights are seldom known in advance since it suggests that a multi-objective approach can generate a solution close to the user preferred one without having knowledge of user preferences

Enumeration of Pareto optimal multi-criteria spanning trees - A proof of the incorrectness of Zhou and Gen's proposed algorithm

The minimum spanning tree (MST) problem is a well-known optimization problem of major significance in operational research. In the multi-criteria MST (mc-MST) problem, the scalar edge weights of the MST problem are replaced by vectors, and the aim is to find the complete set of Pareto optimal minimum-weight spanning trees. This problem is NP-hard and so approximate methods must be used if one is to tackle it efficiently. In an article previously published in this journal, a genetic algorithm (GA) was put forward for the mc-MST. To evaluate the GA, the solution sets generated by it were compared with solution sets from a proposed (exponential time) algorithm for enumerating all Pareto optimal spanning trees. However, the proposed enumeration algorithm that was used is not correct for two reasons: (1) It does not guarantee that all Pareto optimal minimum-weight spanning trees are returned. (2) It does not guarantee that those trees that are returned are Pareto optimal. In this short paper we prove these two theorems. © 2002 Elsevier Science B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Autarkic energy systems : balancing supply and demand with energy storage and controls in local energy micro-grids

When visioning for best possible future energy systems in a world with growing populations, limited fossil fuel resources, rising energy prices and less energy security - more individuals, communities and cities are looking to utilise autarkic principles to harvest, store and optimise use of local energy resources. Energy autarky can be described as a location that relies on its own energy resources for generating the useful energy required to sustain the society within that region or a situation in which a region does not import substantial amounts of energy resources. Functioning autarkic energy systems typically require a micro-grid, well understood energy demand and supply characteristics, opportunities for energy storage of various types and controls able to manage the harmonisation of system components. A critical additional ingredient is users who can work within constraints created by the adoption of autarkic principles. To elaborate the challenges and explore the issues involved with autarkic energy concepts this paper reports on the output from a workshop convened to investigate the role that energy autarky might play in delivering societies able to deliver the ambitious renewable generation targets set by both Scottish and UK Governments. In addition, monitored data from a community micro-grid system in Northern Scotland is analysed and presented to provide additional understanding of the complexities and opportunities created by an autarkic approach. The output from the workshop identified that whilst it is probable that a dogmatic interpretation of energy autarkic will not be universally applicable, the underlying principles represent a bottom up way of widening participation in the development of future energy provision models. Whilst a number of issues and barriers were raised regarding its adoption, the attendees recognised that energy autarky represented a very positive and empowering vision for translating global scale issues to local energy transition. The analysis of monitored generation and demand data from a community micro-grid underlined the problems associated with supply-demand matching with intermittent generation and the need to place an emphasis on the community or entity as an open system that is able to participate in a full range of trading opportunities. Similarities were found between the types of behaviour necessary to create load responses relevant to energy networks containing large penetrations of renewable generation and communities set up along energy autarkic principles