8,476 research outputs found
Enhanced Symbiotic Organisms Search (ESOS) for Global Numerical Optimization
Symbiotic organisms search (SOS) is a simple yet effective metaheuristic algorithm to solve a wide variety of optimization problems. Many studies have been carried out to improve the performance of the SOS algorithm. This research
proposes an improved version of the SOS algorithm called the “enhanced symbiotic organisms search” (ESOS) for global numerical optimization. The conventional SOS is modified by implementing a new searching formula into the parasitism phase to produce a better searching capability. The performance of the
ESOS is verified using 26 benchmark functions and one structural engineering design problem. The results are then compared with existing metaheuristic optimization methods. The obtained results show that the ESOS gives a competitive and effective performance for global numerical optimization
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling
Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02
Shipping Configuration Optimization with Topology-Based Guided Local Search for Irregular Shaped Shipments
Manufacturer that uses containers to ship products always works to optimize the space inside the containers. Container loading problems (CLP) are widely encountered in forms of raw material flow and handling, product shipments, warehouse management, facility floor planning, as well as strip-packing nesting problems.Investigations and research conducted two decades ago were logistic orientated, on the basis of the empirical approaches
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
Economic Load Dispatch depicts a fundamental role in the operation of power
systems, as it decreases the environmental load, minimizes the operating cost,
and preserves energy resources. The optimal solution to Economic Load Dispatch
problems and various constraints can be obtained by evolving several
evolutionary and swarm-based algorithms. The major drawback to swarm-based
algorithms is premature convergence towards an optimal solution. Fitness
Dependent Optimizer is a novel optimization algorithm stimulated by the
decision-making and reproductive process of bee swarming. Fitness Dependent
Optimizer (FDO) examines the search spaces based on the searching approach of
Particle Swarm Optimization. To calculate the pace, the fitness function is
utilized to generate weights that direct the search agents in the phases of
exploitation and exploration. In this research, the authors have carried out
Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by
reducing fuel cost, emission allocation, and transmission loss. Moreover, the
authors have enhanced a novel variant of Fitness Dependent Optimizer, which
incorporates novel population initialization techniques and dynamically
employed sine maps to select the weight factor for Fitness Dependent Optimizer.
The enhanced population initialization approach incorporates a quasi-random
Sabol sequence to generate the initial solution in the multi-dimensional search
space. A standard 24-unit system is employed for experimental evaluation with
different power demands. Empirical results obtained using the enhanced variant
of the Fitness Dependent Optimizer demonstrate superior performance in terms of
low transmission loss, low fuel cost, and low emission allocation compared to
the conventional Fitness Dependent Optimizer. The experimental study obtained
7.94E-12.Comment: 42 page
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