Optimisation of tower site locations for camera-based wildfire detection systems

Early forest fire detection can effectively be achieved by systems of specialised tower-mounted cameras. With the aim of maximising system visibility of smoke above a prescribed region, the process of selecting multiple tower sites from a large number of potential site locations is a complex combinatorial optimisation problem. Historically, these systems have been planned by foresters and locals with intimate knowledge of the terrain rather than by computational optimisation tools. When entering vast new territories, however, such knowledge and expertise may not be available to system planners. A tower site-selection optimisation framework that may be used in such circumstances is described in this paper. Metaheuristics are used to determine candidate site layouts for an area in the Nelspruit region in South Africa currently monitored by the ForestWatch detection system. Visibility cover superior to that of the existing system in the region is achieved and obtained in several days, whereas traditional approaches normally require months of speculation and planning. Following the results presented here, the optimisation framework is earmarked for use in future ForestWatch system planning

Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

The review of multiple evolutionary searches and multi-objective evolutionary algorithms

Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has been identified as an effective method to search for optimal solutions of multi-objective optimization problems (MOPs). The existing multi-objective evolutionary algorithms that benefit from the multiple local searches (multiple-MOEAs, or MMOEAs) use different dividing methods and/or collaborations (information sharing) strategies between the created divisions. Their local evolutionary searches are implicitly or explicitly guided toward a part of global optimal solutions instead of converging to local ones in some divisions. In this reviewed paper, the dividing methods and the collaborations strategies are reviewed, while their advantage and disadvantage are mentioned