Mohammed El-Abd

Performance assessment of foraging algorithms vs. evolutionary algorithms

The class of foraging algorithms is a relatively new field based on mimicking the foraging behavior of animals, insects, birds or fish in order to develop efficient optimization algorithms. The artificial bee colony (ABC), the bees algorithm (BA), ant colony optimization (ACO), and bacterial foraging optimization algorithms (BFOA) are examples of this class to name a few. This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. To the best of our knowledge, most of the work conducted so far using foraging algorithms has been tested on classical functions. This work provides the comparison using the well-known CEC05 benchmark functions based on the solution reached, the success rate, and the performance rate.

A taxonomy of cooperative search algorithms

A lot of heuristic approaches have been explored in the last two decades in order to tackle large size optimization problems. These areas include parallel meta-heuristics, hybrid meta-heuristic, and cooperative search algorithms. Different taxonomies have been proposed in the literature for parallel and hybrid meta-heuristics. In these taxonomies, one can realize that cooperative search algorithms lie somewhere in between. This paper looks at cooperative search algorithms as a stand alone area. Two different taxonomies of cooperative search algorithm are proposed based on two different criteria. Different implementations in this area are reported and classified using these taxonomies.

An improved global-best harmony search algorithm

This paper introduces an improved global-best harmony search (IGHS) algorithm. The proposed modifications effectively combines a novel improvisation scheme with a previously developed mechanism for updating the pitch adjustment rate (PAR) and the distance bandwidth (bw). The aim of this modification is to efficiently investigate the search space by going through the stages of exploration and exploitation. The proposed algorithm is compared against seven previous modifications to HS using rigorous statistical tests when applied to the CEC05 benchmark functions showing a superior performance on most of the tested functions.

Generalized opposition-based artificial bee colony algorithm

The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.

A cooperative approach to The Artificial Bee Colony algorithm

The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown the ABC superiority in terms of performance when compared to other well-known optimization algorithms. In this paper, the implementation of a Cooperative ABC (CABC) algorithm that is based on the explicit space decomposition approach is investigated. Both the ABC algorithm and its cooperative versions are applied to a well-known set of classical benchmark functions.

Discrete cooperative particle swarm optimization for FPGA placement

Particle swarm optimization (PSO) is a stochastic optimization technique that has been inspired by the movement of birds. On the other hand, the placement problem in field programmable gate arrays (FPGAs) is crucial to achieve the best performance. Simulated annealing algorithms have been widely used to solve the FPGA placement problem. In this paper, a discrete PSO (DPSO) version is applied to the FPGA placement problem to find the optimum logic blocks and IO pins locations in order to minimize the total wire-length. Moreover, a co-operative version of the DPSO (DCPSO) is also proposed for the FPGA placement problem. The problem is entirely solved in the discrete search space and the proposed implementation is applied to several well-known FPGA benchmarks with different dimensionalities. The results are compared to those obtained by the academic versatile place and route (VPR) placement tool, which is based on simulated annealing. Results show that both the DPSO and DCPSO outperform the VPR tool for small and medium-sized problems, with DCPSO having a slight edge over the DPSO technique. For higher-dimensionality problems, the algorithms proposed provide very close results to those achieved by VPR.

Opposition-based artificial bee colony algorithm

The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed opposition-based ABC (OABC) is compared to the performance of ABC and opposition-based Differential Evolution (ODE) when applied to the Black-Box Optimization Benchmarking (BBOB) library introduced in the previous two GECCO conferences.

Information exchange in multiple cooperating swarms

This paper investigates the idea of having two cooperating swarms exchanging information in order to solve an optimization problem. The information being exchanged between the two swarms is an important factor that affects the quality of the obtained solution. The information to be shared between the two swarms has to be carefully selected depending on the model being used. This paper compares two types of information to be exchanged, namely the global best and the best particle. It is shown that exchanging the best particles leads to better results than only sharing the global best. This paper also addresses the idea of exchanging the best p particles between the two swarms.

Testing a Particle Swarm Optimization and Artificial Bee Colony Hybrid algorithm on the CEC13 benchmarks

In this paper we test a hybrid Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithm on the CEC13 testbed. The hybridization technique is a component-based one, where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles.

A hybrid ABC-SPSO algorithm for continuous function optimization

In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.