Summary This paper presents a novel heuristic optimization approach using population variant differential evolution algorithm for solving the multiobjective economic emission load dispatch (EELD) problem. The EELD problem simultaneously takes into consideration the effect of gaseous pollutants like NOx, SOx, etc, including the cost of the fossil fuel used in the thermal power plants. A population refreshment mechanism, based on the concept of interquartile range, has been applied to the classical differential evolution method to compute the EELD problem successfully. The algorithm has been tested on IEEE‐30 bus 6‐generator test system and a standard 40‐generator test system, taking valve point loading effects into consideration for effective solutions. The results have been compared with the standard existing techniques in the literature such as linear programming, multiobjective stochastic search technique, nondominated sorting genetic algorithm, fuzzy clustering particle swarm optimization, and modified bacterial foraging algorithm, which authenticate the capability of the proposed algorithm. A novel concept of applying the proposed methodology to a deregulated power market has also been presented in this paper, where successful results have been obtained. This method appears as an efficient and robust optimization algorithm in terms of minimizing the total cost, emission, and computational time.

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This paper presents a weight improved particle swarm optimization (WIPSO)

This paper yields glowworm swarm optimization technique to solve the dynamic economic load dispatch (DELD) problem for minimizing the operation cost in most economical manner. The glowworm swarm based algorithm is considered as meta-heuristic based evolutionary approaches where the agents communicate with each other via bioluminescent glowing, that enables them to explore the objective function more effectively. The effectiveness of the proposed method is demonstrated through five unit test systems. The results are compared with the other optimization technique reported in the literature. The fuel cost and other performance of the given approach has been quite impressive. It is shown that the GSO approach can provide the global or near global optimum solutions with a lesser computational time along with higher efficiency and robustness.

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