This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multiobjective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem.

BackgroundLymphatic filarial parasites survive within the lymphatic vessels for years despite the complex immune environment surrounding them. Parasites possibly accomplish this by adopting various immunomodulatory strategies, which include release of glutathione-S-transferases (GSTs) that counteract the oxidative free radicals produced by the host. Since GSTs produced by parasites appear to be critical for the survival of parasites in the host, several studies evaluated the potential of parasite GSTs as vaccine candidates especially against schistosomiasis, fascioliasis and Seteria cervi. However, vaccine potential of GSTs of lymphatic filarial parasites has not been evaluated before.Methods/Principal FindingsIn the present study, the GST gene was cloned from the third stage larval (L3) cDNA libraries of Wuchereria bancrofti, and recombinant GST (WbGST) was expressed and purified. Serum samples from individuals living in an endemic area were analyzed for their reactivity with rWbGST. These findings showed that sera from endemic normal individuals (EN) carry significant levels of anti-WbGST IgG antibodies compared to subjects who are microfilaraemic (Mf) or show symptoms of clinical pathology (CP). Isotype analysis of the anti-WbGST IgG antibodies showed a predominance of IgG1 and IgG3 antibodies in EN individuals. Subsequent functional analysis of the rWbGST showed that the rWbGST protein retained the enzymatic activity of GST and the antibodies in EN sera could inhibit this enzymatic activity. Similar results were obtained when anti-rWbGST antibodies raised in mice were used in the neutralization assay. Brugia malayi GST and WbGST show significant sequence similarity. Therefore, to evaluate the vaccine potential of rWbGST, we used B. malayi L3 as challenge parasites. Vaccine potential of rWbGST was initially evaluated by confirming the role of human and mice WbGST antibodies in an antibody dependent cellular cytotoxicity (ADCC) assay. Subsequent vaccination studies in a jird model showed that approximately 61% protection could be achieved against a B. malayi L3 challenge infection in jirds immunized with rWbGST.ConclusionsResults of this study show that rWbGST is a potential vaccine candidate against lymphatic filariasis. Nearly 61% protection can be achieved against a B. malayi challenge infection in a jird model. The study also showed that the WbGST protein retained the enzymatic activity of GST and this enzymatic activity appears to be critical for the survival of the parasite in the host.

Abstract:A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-MOPSO approach is first tested for few test problems taken from the literature and evaluated with standard performance measures. It is found that the EM-MOPSO yields efficient solutions in terms of giving a wide spread of solutions with good convergence to true Pareto optimal solutions. On achieving good results for test cases, the approach was applied to a case study of multi-objective reservoir operation problem, namely the Bhadra reservoir system in India. The solutions of EM-MOPSOs yield a trade-off curve/surface, identifying a set of alternatives that define optimal solutions to the problem. Finally, to facilitate easy implementation for the reservoir operator, a simple but effective decision-making approach was presented. The results obtained show that the proposed approach is a viable alternative to solve multi-objective water resources and hydrology problems.

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