A fuzzified multi objective Interactive Honey Bee Mating Optimization for Environmental/Economic Power Dispatch with valve point effect

In this paper, an improved multi objective Interactive Honey Bee Mating Optimization (IHBMO) is proposed to find the feasible optimal solution of the Environmental/Economic Power Dispatch (EED) problem with considering operational constraints of the generators. The EED problem is an important issue in power industry with considered the production of environmental pollution caused by fossil fuel consumption such as dangerous gases and carbon monoxide. The EED problem is formulated as a nonlinear constrained multi objective optimization problem which is solved by multi objective IHBMO techniques that has a strong ability to find the most optimal results. The three conflicting and non-commensurable: fuel cost, pollutant emissions and system loss, should be minimized simultaneously while satisfying certain system constraints. For achieve a good design with different solutions in a multi objective optimization problem, Pareto dominance concept is used to generate and sort the dominated and non-dominated solutions. Also, fuzzy set theory is employed to extract the best compromise solution. The propose method has been individually examined and applied to the standard IEEE 30-bus 6-generator, IEEE 180-bus fourteen generator and 40 generating unit (with valve point effect) test systems. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms such as NSGA, NPGA, SPEA, MOPSO, MODE and MOHBMO. The computational results reveal that the multi objective IHBMO algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms. Also, the results confirm its great potential in handling the multi-objective problems in power systems.

► We proposed a new fuzzy theory for multi objective IHBMO.
► We enhanced capability search for original HBMO.
► Use three test systems that concluded real power system.
► Analyzed the valve point, cost, power loss and emission.
► Used non-dominated search to achieved best Pareto fronts.