A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of the proposed approach is integrating the merits of both TR and PSO, where TR has provided the initial set (close to the Pareto set as possible and the reference point of the decision maker) followed by PSO to improve the quality of the solutions and get all the points on the Pareto frontier. The performance of the proposed algorithm is tested on standard IEEE 30-bus 6-genrator test system and is compared with conventional methods. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions in one single run. The comparison with the classical methods demonstrates the superiority of the proposed approach and confirms its potential to solve the multi-objective EED problem.
An interesting approach for the design of anti-allergies is rationally considered. It was proved that current anti-allergic drugs comprise piperazine and acrylic acid segments. In harmony with these findings, new products 5a-u were synthesized starting from conjugated 2-thiopheneacrylic acid with amino acid esters3a-g followed by coupling of their acid derivatives4a-g with some piperazine segments, with the aim to increase their biological activities and decrease side effects. The anti-allergic and anti-inflammatory activities of the products were evaluated and promising results were obtained.
In this paper we
present a new optimization algorithm, and the proposed algorithm operates in
two phases. In the first one, multiobjective version of genetic algorithm is
used as search engine in order to generate approximate true Pareto front. This
algorithm is based on concept of co-evolution and repair algorithm for
handling nonlinear constraints. Also it maintains a finite-sized archive of
non-dominated solutions which gets iteratively updated in the presence of new
solutions based on the concept e-dominance. Then,
in the second stage, rough set theory is adopted as local search engine in
order to improve the spread of the solutions found so far. The results, provided
by the proposed algorithm for benchmark problems, are promising when compared
with exiting well-known algorithms. Also, our results suggest that our
algorithm is better applicable for solving real-world application problems.