Projectile-target search algorithm: a stochastic metaheuristic optimization technique

This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithm

An Improved Artificial Bee Colony Algorithm for Staged Search

Artificial Bee Colony(ABC) or its improved algorithms used in solving high dimensional complex function optimization issues has some disadvantages, such as lower convergence, lower solution precision, lots of control parameters of improved algorithms, easy to fall into a local optimum solution. In this letter, we propose an improved ABC of staged search. This new algorithm designs staged employed bee search strategy which makes that employed bee has different search characters in different stages. That reduces probability of falling into local extreme value. It defines the escape radius which can guide precocious individual to jump local extreme value and avoid the blindness of flight behavior. Meanwhile, we adopt initialization strategy combining uniform distribution and backward learning to prompt initial solution with uniform distribution and better quality. Finally, we make simulation experiments for eight typical high dimensional complex functions. Results show that the improved algorithm has a higher solution precision and faster convergence rate which is more suitable for solving high dimensional complex functions

Development and applications of various optimization algorithms for diesel engine combustion and emissions optimization

For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows

The vehicle routing problem with time windows (VRPTW) is a non-deterministictime hard (NP-hard) with combinatorial optimization problem (COP). The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. While MABC has been reported to be successful, it does have some drawbacks, including a lack of neighbourhood structure selection during the intensification process, a lack of knowledge in population initialization, and occasional stops proceeding the global optimum. This study proposes an enhanced Modified ABC (E-MABC) algorithm which includes (i) N-MABC that overcomes the shortage of neighborhood selection by exchanging the neighborhood structure between two different routes in the solution; (ii) MABC-ACS that solves the issues of knowledge absence in MABC population initialization by incorporating ant colony system heuristics, and (iii) PMABC which addresses the occasional stops proceeding to the global optimum by introducing perturbation that accepts an abandoned solution and jumps out of a local optimum. The proposed algorithm was evaluated using benchmark datasets comprising 56 VRPTW instances and 56 Pickup and Delivery Problems with Time Windows (PDPTW). The performance has been measured using the travelled distance (TD) and the number of deployed vehicles (NV). The results showed that the proposed E-MABC has lower TD and NV than the benchmarked MABC and other algorithms. The E-MABC algorithm is better than the MABC by 96.62%, MOLNS by 87.5%, GAPSO by 53.57%, MODLEM by 76.78%, and RRGA by 42.85% in terms of TD. Additionally, the E-MABC algorithm is better than the MABC by 42.85%, MOLNS by 17.85%, GA-PSO and RRGA by 28.57%, and MODLEN by 46.42% in terms of NV. This indicates that the proposed E-MABC algorithm is promising and effective for the VRPTW and PDPTW, and thus can compete in other routing problems and COPs

Optimization of multi-injection diesel combustion through direct application of ABC and PSO variant algorithms

In this study a modified artificial bee colony algorithm and the cooperative-swarm variant of particle swarm optimization were applied to minimize diesel engine emissions and fuel consumption in the laboratory at medium load conditions. Tests were conducted using No. 2 diesel fuel in a four-cylinder, production diesel engine with series turbochargers and a high-pressure exhaust gas recirculation loop. Emissions were recorded at steady-state conditions and input into custom scripts in Matlab. Both triple-injection strategies, consisting of a pilot-main-post injection scheme, and quadruple-injection strategies, using two pilots, were investigated for a high exhaust gas recirculation rate of 38%. A two-factor design of experiments study was also completed to examine the individual and interaction effects of six variables when using three injections. The modified artificial bee colony algorithm achieved 40% reductions in soot and nitric oxide emissions within 176 engine runs using a triple injection schedule with six variables. The cooperative-particle swarm method optimized an eight variable, quadruple injection schedule in only 84 engine tests. Cooperative-particle swarm algorithm was unable to find a similar optimum to artificial bee colony in triple injection experiments and appeared to stagnate. A longer burn time was observed with the quadruple injections which also displayed decreased maximum cylinder pressures, maximum cylinder pressure rise rates, and fuel consumption results. Triple injections were able to achieve lower nitric oxide emissions. Optimized triple and quadruple injection schedules called for similar centers of combustion early in the expansion stroke resulting in similar hydrocarbon, soot, and carbon monoxide emissions. Results of the design of experiments testing illustrated the strong effect of main injection timing and fuel pressure on all aspects of the objective function. Limited effects were observed from interaction terms, except in the case of carbon monoxide and hydrocarbons