New directional bat algorithm for continuous optimization problems (1805.05854v1)

Abstract: Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.

• The paper introduces a directional enhancement to the bat algorithm that improves exploration in high-dimensional search landscapes.
• It employs dual-directional echolocation and adaptive local search strategies to prevent premature convergence.
• Empirical tests with benchmark functions show the method consistently outperforms traditional BA and other bio-inspired algorithms.

Enhanced Bat Algorithm for Continuous Optimization: Directional Echolocation Approach

The paper "New Directional Bat Algorithm for Continuous Optimization Problems" introduces an enhanced variant of the standard Bat Algorithm (BA) through integration of directional echolocation and augmented search features. The authors aim to tackle the premature convergence issues associated with the Bat Algorithm when applied to complex, high-dimensional optimization problems. This paper is a valuable contribution to the field of swarm intelligence-based optimization, focusing on increasing both exploration and exploitation capabilities of BA.

The Bat Algorithm, originally inspired by the echolocation behavior of micro-bats, is widely recognized for its efficiency in solving low-dimensional optimization problems. However, it tends to underperform in higher dimensional spaces, often succumbing to local optima. The proposed Directional Bat Algorithm (dBA) addresses this by introducing directional echolocation, allowing bats within the algorithm to emit pulses in multiple directions towards the best-known solutions and a randomly selected solution. This dual-pulse mechanism aspires to guide the algorithm more effectively through complex search landscapes, mitigating risks of local entrapment.

Key Modifications

1. Directional Echolocation: The introduction of directional echolocation within dBA allows for strategic exploration based on the comparative fitness of directional pulses. The bats’ movements are influenced by both the globally best-known solution and a randomly selected solution, thus enhancing exploration at early stages of the optimization process.
2. Enhanced Local Search: dBA adapts local search by incorporating a monotonically decreasing weight and adding randomness through controlled steps. This dynamic facilitates an adaptive balance between global and local search phases, pivotal in adjusting search intensity based on iteration progress.
3. Adaptive Pulse Emission and Loudness Control: The adaptation of pulse emission rates and loudness within dBA provides an automatic switch from exploration to exploitation as iterations proceed. By managing these parameters adaptively, dBA adjusts its search tactics according to current landscape and iteration stage.
4. Solution Acceptance Criterion: Unlike the standard BA, which solely updates the global best position, dBA considers both global and local improvements for solution acceptance, even permitting updates with inferior computed values to maintain broader search diversity.

Empirical Evaluation and Comparative Analysis

The efficacy of dBA is rigorously tested against a series of benchmark functions, including standard and the non-standard CEC'2005 benchmark suite. The numerical results from these experiments indicate that the dBA consistently outperforms traditional BA, standard bio-inspired algorithms such as PSO and GA, as well as several BA variants known in literature.

Statistical analyses, including non-parametric pairwise and multiple comparison tests (e.g., Friedman's and Quade test), affirm the superior performance of dBA, demonstrating significant improvements particularly in high-dimensional contexts. In the first experiment alone, dBA demonstrated enhanced performance on nine functions and significantly outperformed other algorithms in terms of efficiency and robustness in various scenarios.

Implications and Future Directions

The paper's implications for optimization in swarm intelligence are significant. The integration of directional echolocation combined with adaptive search parameter control exemplifies a strategic enhancement to optimize complex problems. This approach not only improves the algorithm's performance but also opens up new avenues for further exploration into multi-directional search strategies using bio-inspired processes.

Future work may involve extending this methodological framework to accommodate multiple pulse emissions in varied dimensions and scenarios or applying the refined algorithm to real-world complex problems, thereby advancing its applicability across diverse domains. Additionally, exploration of hyperparameter tuning and dynamic strategy adaptation could further refine the proposed algorithm's efficacy.

The Directional Bat Algorithm presents a structurally sophisticated enhancement to swarm intelligence techniques, offering a robust tool for tackling the multifaceted challenges of high-dimensional continuous optimization. With ongoing advancements, dBA signifies a robust addition to the evolutionary computation toolkit, especially for applications demanding dynamic exploration-exploitation balance.