Previous studies are focused on the cogeneration unit commitment problem (CUCP) to find optimum operating cost with satisfaction of operation constraints. However, the profit and environment emission are not considered, the obtained CUCP problem is not enough to address the multi-objective profit based CUCP (MO-PBCUCP). The goal is to find optimum heat and power generation schedule of cogeneration system to increase the profit and decrease environmental emissions simultaneously for Generation Companies. Therefore, the MO-PBCUCP has been solved to find schedule of cogeneration, conventional thermal power and heat units. The optimization technique such as continuous/binary particle swarm search algorithm and civilized swarm/successive approximation with decision making strategy approach is implemented to resolve mixed-integer MO-PBCUCP. These methods have been applied to cogeneration based test system, to verify the effectiveness of the optimization method. The implemented method optimizes the opportunities to make use of high profits and minimize emission. The obtained profit from civilized swarm and successive approximation is high as compared to continuous/binary particle swarm search algorithm and the emission is less.

以前的研究集中在热电联产机组承诺问题（CUCP）上，以找到满足运行约束的最佳运行成本。然而，不考虑利润和环境排放，得到的CUCP问题不足以解决基于多目标利润的CUCP（MO-PBCUCP）。目标是找到热电联产系统的最佳热量和发电计划，以同时为发电公司增加利润并减少环境排放。因此，MO-PBCUCP 已经解决了寻找热电联产、常规火电和热力机组的时间表。采用连续/二值粒子群搜索算法、文明群/逐次逼近与决策策略方法等优化技术求解混合整数MO-PBCUCP。这些方法已应用于基于热电联产的测试系统，验证了优化方法的有效性。所实施的方法优化了利用高利润和减少排放的机会。与连续/二元粒子群搜索算法相比，文明群体和逐次逼近获得的收益高，排放量少。