Summary Electricity, heating and cooling usage at the same time constitute an important part of primary energy consumption. Currently, intense efforts are being devoted to the improvement and development of new technologies to promote energy savings and to mitigate the global warming. Among these technologies, tri‐generation and poly‐generation systems come into prominence due to their high efficiencies, as well as low operating costs and low greenhouse gas emissions. Nevertheless, modelling and sizing these systems is a large‐sized problem itself, and the higher the number of inputs, the higher the computational complexity. This paper aims to give a comprehensive review of the optimisation techniques used in tri‐generation and poly‐generation systems modelling and optimisation. After having reviewed more than a hundred papers, taxonomy is constructed. Self‐organising map is used to cluster the reviewed studies based on the optimisation techniques. Variations of the energy resources and the outputs of implemented systems are summarised. The crowd of clusters demonstrated the widely used optimisation techniques and the gaps in research. This study will certainly pioneer the optimisation studies in the field of tri‐generation/poly‐generation applications. Copyright © 2015 John Wiley & Sons, Ltd.

Complex problems of the current business world need new approaches and new computational algorithms for solution. Majority of the issues need analysis from different angles, and hence, multi-objective solutions are more widely used. One of the recently well-accepted computational algorithms is Multi-objective Particle Swarm Optimization (MOPSO). This is an easily implemented and high time performance nature-inspired approach; however, the best solutions are not found for archiving, solution updating, and fast convergence problems faced in certain cases. This study investigates the previously proposed solutions for creating diversity in using MOPSO and proposes using random immigrants approach. Application of the proposed solution is tested in four different sets using Generational Distance, Spacing, Error Ratio, and Run Time performance measures. The achieved results are statistically tested against mutation-based diversity for all four performance metrics. Advantages of this new approach will support the metaheuristic researchers.

Multidimensional Knapsack problem (MKP) is a well-known, NP-hard combinatorial optimization problem. Several metaheuristics or exact algorithms have been proposed to solve stationary MKP. This study aims to solve this difficult problem with dynamic conditions, testing a new evolutionary algorithm. In the present study, the Partheno-genetic algorithm (PGA) is tested by evolving parameters in time. Originality of the study is based on comparing the performances in static and dynamic conditions. First the effectiveness of the PGA is tested on both the stationary, and the dynamic MKP. Then, the improvements with different random restarting schemes are observed. The PGA achievements are shown in statistical and graphical analysis.

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