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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.

A novel hierarchical integrated system optimization and parameter estimation technique is described which determines the optimum steady state operation of an interconnected industrial process in spite of deficiencies in the model. The technique is iterative and involves successive solutions of system optimization and model parameter estimation problems, utilizing information feedback from the real process. Particular emphasis is given to a strategy where the coordination task is divided into two nested iterative loops, with the inner loop optimization containing a self-adaptive model. Derivatives of real process measurements required by the outer loop iteration are also used to update the model. New modifiers are introduced to cater for the output-dependent constraints. This model-based doubleloop iterative strategy retains an important practical advantage in that it reduces the required number of set point changes to the real process. Optimality properties and convergence conditions are investigated. A simulation study is also presented.

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