Hybrid tabu search – strawberry algorithm for multidimensional knapsack problem

Multidimensional Knapsack Problem (MKP) has been widely used to model real-life combinatorial problems. It is also used extensively in experiments to test the performances of metaheuristic algorithms and their hybrids. For example, Tabu Search (TS) has been successfully hybridized with other techniques, including particle swarm optimization (PSO) algorithm and the two-stage TS algorithm to solve MKP. In 2011, a new metaheuristic known as Strawberry algorithm (SBA) was initiated. Since then, it has been vastly applied to solve engineering problems. However, SBA has never been deployed to solve MKP. Therefore, a new hybrid of TS-SBA is proposed in this study to solve MKP with the objective of maximizing the total profit. The Greedy heuristics by ratio was employed to construct an initial solution. Next, the solution was enhanced by using the hybrid TS-SBA. The parameters setting to run the hybrid TS-SBA was determined by using a combination of Factorial Design of Experiments and Decision Tree Data Mining methods. Finally, the hybrid TS-SBA was evaluated using an MKP benchmark problem. It consisted of 270 test problems with different sizes of constraints and decision variables. The findings revealed that on average the hybrid TS-SBA was able to increase 1.97% profit of the initial solution. However, the best-known solution from past studies seemed to outperform the hybrid TS-SBA with an average difference of 3.69%. Notably, the novel hybrid TS-SBA proposed in this study may facilitate decisionmakers to solve real applications of MKP. It may also be applied to solve other variants of knapsack problems (KPs) with minor modifications

Superstructure optimization and forecasting of decentralized energy generation based on palm oil biomass

Malaysia realizes the importance of addressing the concern of energy security to accomplish the nation’s policy objectives by mitigating the issues of security, energy efficiency and environmental impacts. To meet the rising demand for energy and incorporation of Green Technology in the national policy, Malaysian government during the last three decades has developed several strategies and policies. National Green Technology Policy was an initiative, which marked the firm determination of the government to incorporate Green Technology in the nation’s economy policy. Malaysia has abundant biomass resources, especially oil palm residues with power generation potential of about 2400 MW, which is promising for decentralized electricity generation (DEG). The aim of this study is to determine the best location to install appropriate biomass electricity generation plant in Johor and forecasting the electricity market (i.e. electricity demand) in order to provide a strategic assessment of measures for the local energy planners of Malaysia, as an optimization bottom-up model. A superstructure was developed and optimized to represent DEG system. The problem was formulated as Mixed Integer Nonlinear Programming (MINLP) and implemented in General Algebraic Modeling System (GAMS). Electricity demand was modeled using Adaptive Neuro Fuzzy Inference System (ANFIS). Based on GAMS and ANFIS models, palm oil biomass based DEG system and distribution network scenarios for current as well as next ten, twenty and thirty years have been proposed for State of Johor, Malaysia. Biomass from sixty six Palm Oil Mills (POMs) would be collected and transported to eight selected locations. Empirical findings of this study suggested that total production cost is minimized by placing biomass gasification based integrated combine cycle (BIGCC) power plant of 50MW at all eight locations. For 2020 Scenario, no additional infrastructure will be required. For 2030 Scenario, additional units of BIGCC of 50MW will be required at five out of eight locations. While for 2040 Scenario, again no additional infrastructure development will be needed. Total minimum cost varied from 6.31 M$/yr for current scenario to 22.63 M$/yr for 2040 scenario