Whale optimization algorithm (WOA) is a recent nature-inspired metaheuristic that mimics the cooperative life of humpback whales and their spiral-shaped hunting mechanism. In this research, it is first argued that the exploitation tendency of WOA is limited and can be considered as one of the main drawbacks of this algorithm. In order to mitigate the problems of immature convergence and stagnation problems, the exploitative and exploratory capabilities of modified WOA in conjunction with a learning mechanism are improved. In this regard, the proposed WOA with associative learning approaches is combined with a recent variant of hill climbing local search to further enhance the exploitation process. The improved algorithm is then employed to tackle a wide range of numerical optimization problems. The results are compared with different well-known and novel techniques on multi-dimensional classic problems and new CEC 2017 test suite. The extensive experiments and statistical tests show the superiority of the proposed BMWOA compared to WOA and several well-established algorithms.

An enhanced associative learning-based exploratory whale optimizer for global optimization

The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0–1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Levy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0–1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0–1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library.

A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem

Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

The quadratic assignment problem (QAP) has considered one of the most significant combinatorial optimization problems due to its variant and significant applications in real life such as scheduling, production, computer manufacture, chemistry, facility location, communication, and other fields. QAP is NP-hard problem that is impossible to be solved in polynomial time when the problem size increases, hence heuristic and metaheuristic approaches are utilized for solving the problem instead of exact approaches, because these approaches achieve quality in the solution in short computation time. The objectives of this paper are to describe QAP in details showing its types, nature of the problem, complexity of the problem, importance, and simple example. QAP formulations, problems related with QAP, solution techniques, QAP benchmark instances, applications of QAP, survey of QAP researches are also illustrated.

A comprehensive review of quadratic assignment problem: variants, hybrids and applications

A Hybrid Method Integrating an Elite Genetic Algorithm with Tabu Search for the Quadratic Assignment Problem

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them.

A comprehensive study of cuckoo-inspired algorithms

The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In This paper, we present an Elite Opposition-Flower Pollination Algorithm (EOFPA) for solving Quadratic Assignment Problems. The performance of the proposed algorithm is tested against a set of benchmarks of QAP from the public QAPLIB Library. we compare the algorithm against the best proposals from the related literatures and we determine that the proposed algorithm is superior to some other algorithms.

Lila Abdel-Fatah

Papers

A comprehensive study of cuckoo-inspired algorithms

Elite opposition-flower pollination algorithm for quadratic assignment problem